{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One step univariate model - ARIMA\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training an ARIMA times series forecasting model\n",
    "- implement a simple ARIMA model to forecast the next HORIZON steps ahead (time *t+1* through *t+HORIZON*) in the time series\n",
    "- evaluate the model on a test dataset\n",
    "\n",
    "\n",
    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n",
    "\n",
    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import math\n",
    "\n",
    "from pandas.tools.plotting import autocorrelation_plot\n",
    "from pyramid.arima import auto_arima\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from common.utils import load_data, mape\n",
    "\n",
    "%matplotlib inline\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n",
    "\n",
    "# Set for demo purposes (shorter run)\n",
    "demo = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data from csv into a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2012-01-01 00:00:00 2,698.00\n",
       "2012-01-01 01:00:00 2,558.00\n",
       "2012-01-01 02:00:00 2,444.00\n",
       "2012-01-01 03:00:00 2,402.00\n",
       "2012-01-01 04:00:00 2,403.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('../data')[['load']]\n",
    "energy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot all available load data (January 2012 to Dec 2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HNB0ihLikCn3K/PlFS1B+0t7Hww4DdtvNjSykudGtAkYph1w5JFQOCUmIypZ/9Oja5yoM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\niIiIiAjMHBIRERERERGA/wNQA9G0HxP+TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d051e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot first week of July 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nN9wAzzwDixf7jiQ8UusNo1A5BK07FBERAXjlFTcY8vTTs3P8nBx49lk3wfT8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\nRg0/MWzc6K7CqZqUGWGeWKoKcfrilhw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POMF3JH6khpNE4f8DJYfB0J+8LNKbS3+Mcb/o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\niKDkUERERERERFByKCIiIiIiIsD/B2XBUr9Eb1QlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d004ad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create training and testing data sets\n",
    "\n",
    "We separate our dataset into train and test sets. We train the model on the train set. After the model has finished training, we evaluate the model on the test set. We must ensure that the test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n",
    "\n",
    "We will allocate the period 1st September 2014 to 31st October to training set (2 months) and the period 1st November 2014 to 31st December 2014 to the test set (2 months). Since this is daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in the recent days. Therefore, using a relatively small window of time for training the data should be sufficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_start_dt = '2014-10-01 00:00:00'\n",
    "\n",
    "if(not demo):\n",
    "    test_start_dt = '2014-11-01 00:00:00'\n",
    "else: \n",
    "    test_start_dt = '2014-12-30 00:00:00'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OV5AlavEvcc50QRpTWKkkt1VKmCumBeqmvk3YP7HfnnPoD6sziJGmcg4FxZja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VaUI0TNu4IqkHpGrm1MkD1TmkGj0xAxhwREufHQ8V1vr0fg0ATZlLtZ7wyZQybYkNY5pN9TIGHO\nn5OVmufVB2rEDEtKEKHPzdnHuI64Y+STlXL1AtMwhzH2wLKdNLLSb59TwqV7Fu9BOV+O5hY7b0Ee\nb3n2W/CH//YPrX2iMjb9roihpnFLGBYR6NfPa0pZqbktbuw25pAFCAkko5LrWiJRlBxrySIM9aN7\ntlqqjubPPB/W2+vRPWVej9y9pgNx6ZgGsEsLk0hPbcoC0UKMaUjDMYdCljK2L4+yYsuYQya3meuX\nhjl0XY9JALQNZGZZyuKrAIY2F7gdwAegDGW+Zv0LrYVheD4MwxvCMFwIw3BHGIaPC8Pwr4bfvT8M\nwyuGNQ4vCcPwlWEYnjX+/v8Nw3BnGIa7wjC8yfjuWBiGPx6G4XQYho8Kw/CzvvGY4JCT1p04oT6P\nHh2BIxOMkONjUkDgA4ff+54y1XA1H3NIQboZ/K6ujgL6d70L+OIX+fnrrdPxM4dZl7Ig5oaaDg5d\nstIHHlCfX/zi1jOHBDSqVXVcga0Bh5yslMsBPX1asdg//dPAb/1WHPiFYbzAfRCo3+sgRyIrpWOk\ng0NTxpmmcaU8vvpV4BnPUOet2eTzCXXmcGmJZ1d10EuyZRvoJam1jaUsl9U/Yox9rdVvoVauxVYQ\nu4MuSvkSqsUq2v22LOeQAX4UaB6cPYgHVh5g2cV7Fu/BR77zEdx6360j4w7TSMNjFADEcw6BBMyh\nIOfQrBeXD/IROKQ+PultuRBnKbv9LnqDHg7NHcLy5jKqpWrcrbTfxUJlAautVWx2N1EtVVkAyeXU\nmMEfAVE9kODYE8rx5BwrJ5WV+iRxsT4WWaneR8IccqDWletDQVIxL8w5tJx/CnJFOU6ewM7WPnvk\ns3j5j7wcS5tLUbkAiSEPe4yGY+r0O145XLQdoazSB/zGgvEJZKXmtmjsYw6RlmvfJS10zs1zPSaV\nlUoMaSSlXmhenBSYu2brnXq0Xe6+9jKngjFJwLFIeixVDVjYPBFz6LlmOXDoWzyKLVQwxmBJG83R\nvGe5fi5DGtu7wSYr1ftJF0+47UQKDUtLAg5/ESrXEAB+A8BnAXwLGsP3cGocc2iCo3PnFFtx4oQK\nADnTFsqDk9Y7owDc8gyO2s//vHJddG2n3Y6zeXp9ui9/GfjKV+LBb7c7kv0dPqyktRxzqjcChz7T\nGtp+Fk2XAwIjcFivu5nDBx8EnvlMxR5J3UonyTkExsHhVoAjAodkfmOTlRI4fMUrgD/6I14yGYbj\nss8gsAM/vXGunrncSJo8Pz+SlU7SOHDcaqnrmsAhMafmePL58ZxDG4AE7NJb3fxnfd0uK9X7AGq/\ndA3o7Z7Fe3DXybvQ7rUxU5qJPZApV5BWmLlSFnedvAvP+v+ehfuX73fmHBZyBbz2qa9V46ksxPqc\nbyoJyR0P3WEt5SAJollwKMi94ACb3jjgR/OtlWuod+qRrNQV1PcHqg8F7tQWNxaxe3p3xIiSjNMM\n2HdM7UCr10KlWGEDADbwZ8AxFV0fk5UafXqDHh6x6xEAwAaItvwdM0BKK78TyeZM5tADNACLtMwR\nlFmZQ4FsjMbeG/QiwMGxMEmMbVxtvb2OK+aviOZJY5IY8tjY1U6/IzqG5rgnARDm9WFlDj2yUnNb\n0dh1WWkS5tAjmc3KlEPCHCYFq7Z5+ZhDkhZKFAFJJcMi4Ct1hpVIPS33flY5hxJwaGPoAbe8Xdro\nmpIsiqZiDieUlfr6ZMYchmG4Gobh8vD/m2EY/rcwDH93WJT+YdckstLlZeARj1A5eiTXNPuQSQax\njL5GgMfHLulj09ub3gS89KUKgBQK6p/NkOapT1X/uKDeJxk0mw2M3XKLyrkEspeVmnI9U1Zqyzn8\n/vdVrmIYymWlEubQBBEkKwXU+dpKWanUkIbAoW3M3Ly4fklkpdRvamrrZKUERElWyoFjLufQxRzq\nbr4ScOjqMxgoKS8HDq//6+vxlL98Clq9FmaKM3HmcGiMUi2OwGEvHL+IPnrfR/HZhz6L93/r/c5S\nFoVcAQsVVbx15/TOWJ+zG2exp7onKglhk9/5gmgOHPokev1BOkMaYqB0WakpPeW2U8wXY+M5XT+N\n/bX92D+jbhLW9XXQjfLAIsmg0I3PHFOr14o+bbLSftjHJbVL8PGXfxxPOvAkrzwPiB9ridmMSFYK\nWW6aOEAWAGhqOjjU7xGJbAwAfuJvfwLPe9/zxoI2L5vjCexsTS8dQvvgjJ0kzGEhV0AxV0S7345J\nBiUutBLmSAIgXHUOxbJSB0ASM4dBMoAgAkcSWekE+Z1svq0gJ5mceekdYN770jxhq6xUkx+KJLOC\na8S3L8Bx73vYxRhz6BkPYGdpt1pWqucK+hYqs3QrNc+rTzFiu/ZNhUZsG9ZvjBYEQTEIgjcFQXAk\nCIJWEAQPDn8uSbdxoTQyD/HJSnVw2GyqwNyU3y0NMxvPnBn93pWDRDJU+rQ17vk7GADveAfwwQ+O\nCrybga0uK7XNTcIKmc2Wc3jTTcBrFVEhdut861vjrCDXOJaWmEMK/M3zAQD33z8yspHKSrljJCll\nQeBwZkYmK73nHuDnfs7+Pe17asqec6jPnwOHeg6sZGGA+pkgSiorpVYsxlnaNM3FnOrMoQ34mW6l\nNtD/q78KvOhFdkZUB4fmdmhf1IeeA9zc6cHc7reVrNSUOg7BCBXM5pjD75z/Dv7dtf8OXzv1NWsp\nCwokSaK4a3pXrM+55jlcu/tanNs4NyYrNVdifQYgrV4rAk56HxdgSysrpfy+WqmmwGHI5xx+c/Gb\n+PX//euR9JRjF0/VT2H/zH5MF0cPAI6BJcbv1570a1EfPUDkglYTaHT6HYQIo3HbnO0okHz+Vc+P\nmErfyniMYRDklDllpR72II2s1GpI45OVGqUsrLmCxnZuf+B2fObIZ8bkXt7rOqWsVAdt9PessZOH\nOaR7eU915JKlz00ih5yENTYDzYlkpblxBtq8btPkHIoAi8SQRSIrtTGHArDKSWolzCEtQi23llEp\nVNhncRassA0gxGTFHuA3iRmVea/53jOJcg49yootMaQJNYWCQFZqe+/ZmEOXrDQL1jzLnMO3AXgO\ngFdDlYr4NSj30T9IsI0LorVao0CWGicrXV4GrrkG+MAHFDNYrcb7ESA4c0YGjohV8YEj7hn9z/+s\n6rgVCiog5Wr46cyhPrekNfy4cbsMYIjNCgI/c/iGNyiA62sm6Ot0lHQxDBVLUyzG57a5qeTAP/qj\n6ufdu9Mxh1JZqZ5zSKVAXPP/h39Qx8vViKnimMNdu9RihZQ5lCwMmHMLQzs4dB2jUikbcOhiTl2G\nNFLmkOb11rcCH/2ofWEgCXNIzsbcfU0P+FavpWSlA4usVGcOjYf2ifUTeMqBp2Bpc8nrVkrM4a7p\nXbE+K5sruGZBFZO3bce6oqu9uCg30pxnmjzAsT4MK2jmHJJk1Azq3/DpN+BPv/an+NbZb0WmNSHC\niIUBgNON0yw41MdNq/h3v+puvOmGN6m5MQn+sfpjRoDU6DQwU5pBtVRFo9NgAwCa31hulsBsRior\nTSNBsm3HJytljY0Y9iSprJQLkjhwXCmo+j1rrTU2aPMxFUmavoAQgUOJIY8lT5hcgWk7PsASY7NS\nlrIwAV1WslLuHkmTcyh2YhUY0nhlpS4ACT+75gvsufl3+13smt6Fk+sn2Rqnk8hKTRDhA8cS2bl1\nXwaos7msihQqA/dzRiwrdbD4WRnS2J7pZr9KsYJ2jw/66V6RupXGFk9Syq5NhUZsP9Zv4u1nAfx0\nGIafDMPwu2EYfhLAiwF4eJALr5mSUoBnTlZXgcsuUz/fdtsIHJqOlYcOqaBcknNH4HBz0z1GDhx+\n+9sqP3D3buDYsRFzaIKjqfGYzSqbc/Xhxs3lHBaLyijkc59T869UZLLSU6f8fUxwSIDo0CGVV8gx\nVQ8+CFx+uSoED6h8uLTMoc+QxpSV0qdrcYBj7czmynHbu1ctRHDgsF5Xx8zlRGpjDvW5haG6/sxr\n0HWM/uqvgFe9Si1cZMUcmswpMYcbG+pnV86hDUDbQD83LxdzaILDxUX1exYcDoMSm1tpJCvVcw7N\nIGLQxY6pHWh2mxGTZgYkEXM4rZjDWilegqE36OFA7YBiDnVZqScXxHwh2wxpJPIa10uZZQ6HQfZs\neRZrrbUx5lAHfgSKqU8+yCNAMDamxcYi9s7sjYAEEH+R0u+u239dtLIvZRj049joNFAr1TBdnEa9\nU2eBD81P314sQGSCtjRBpCR/yyVP9bKLnLERxxwKZKW+nENz/qutVRTzRVy9cDWOrh1lDWnYgD2t\nrLQ/kpXaDGk4Qx4uQC4EBbzvxe/Dbb9w26gPScIEhjRZOXq6JKNSWanrHkmVcyiodSfpIwFHYiAu\nlJX6lAVhGKI76GJvdS9O1k9GqgGzzySyUsn59zHHSWWltK/Y4hmzeOJiFyfNOXQxh5y8P2mjbUpk\npeW83aWbmD2pW6neT5onagOHWTGHNgsVj7XKhdds4NAEEOUy8KQnqc9GQwWmnLTw8GEVsBMocoEj\nCh59AIpjd777XSVz3bdPGeCUy+nkkBJWCFCA8CUvGc3NZA47HQUkrrxSMSfk4CjJuZPY/5t9ej0F\nPg4etIPD738fuOqq0c/cMcqSOTTB4fy8+9xKwCEnK6Xzum+fAiIcYCEXWv2ZnCbnkLs+uG3px+iX\nf3nE0k6ac2mTlVLOYb0+ynXk5rZ//+iatbGLknn5DGl0AEn5phw4pJIPzW6TZQ5JVkrMYaVQYcsd\nzJZn0ew2o3wnGwsxVZhC+MYwKrxssoILlYXI2IWTlUoNaZKWshiEQllpjs853DezD6cbp6OXZoBg\nzHCm3VcHf6W1EgFcc9wrrRUsVBYiAA2MB5F60K83idSPYw6rpSqmi9NKVso40tH89H2aAIIL7Mw+\nUrdSSaCZmj2w5NOZ25Ewh/oCCieLMoNoYoQPzh1UZVw45tAiT3UFdramz40WKNIY0tC1/eg9jx7V\nC80JmcMEro6TnPtofx5ZqXmPcKxp0pxDCVMlATUSFsZ1X2ciK7UwQzpzyC1oeOuXCsGxlzkWlKlw\nScp9DKwkLzN2XUtyDgWGVeb5yLKUhURW6iq9ROdGIpcFkstKbde+Kd+PbcP6Tbz9PYDbgiB4XhAE\njwqC4PkAPjr8/cOqUb6e3sxAk4BIqQS88pWqrMT0NA8QdHBYLsuYQx845Bbnzp9XBbyvvx74y7/k\ncw5trGAaWelddwG33qpqzHHMITmj7t6txuYqL0GNAJ+LpaRm9qFzcuCAYk4Lhfi4jxxRzCGgJIO/\n8isy5lACosx9dTojUxOS8s7Pu8GRpL4lJyslwLJjh7p+NzZ44ONjxSQ5hxLpKW2LO0ZZyEqDgAfH\n09MjcGibW6WiQFu7LWcOXaygRFZKSgAOHNID+NjaMatbqc4clgtlVnpI4HCzu2mtz8cVSzdfktVS\nFZ1+B51+hzVBkBjS9AY9FAKmzp9PVupYQaU+MbfSIVO6UFlAq9dCu99m3SjbvTb2VPdgZXMlCgZi\nstrWCuan5vHLj/9l3P2qu6NxUx+9KPnY3AQvZBP4tHttlPNlxRy261YJknm8JaygNDdJGkRLpFw6\nyLRKTwWyUl/OIbfq7wv+ljeXsVBZwIHaARxfP84b0gjzwCTNlnOY1pBGb4kBi1BWmrbchT5uZx9T\nDitgaW3MYRJWTLKgIcknFANxiRmTYP7dfhfFXBE7p3dGMn2pIiCx1HPCWphJriOpssJ6PyZgxWhM\nPsMqeodQc+U+S1skBxXISou5Iru/Vq81ZmyTRlaaljV3SfyBZODw/wHwTwD+B4CvA3gXVDmL30mw\njQuiSaRleuC7sDACFGbw224rmSOBw+nprQOHFJC+5CXAl77Eg0OJy6QEQALADTeoz098gs85JHCo\n58FxtffMOQDp2EUChzt3KvaMyzlcWlJgFVBmI7WajDlMU8pCP446cziprJRjYAmwBYECiEtLbidO\nfcxpmEOf9NS2rawMaWwGMC5wqI9nz9DfwbxmJaBfYkhjA4ec0VSr18KhuUOqoNaXwKwAACAASURB\nVDpX53AYJBBzuLOyM/bQ7va7mC3PYrO7iWa3aS3B4AWHw5dkpVBBvV1nZTG2l3ZMppMg8AeGzKGw\nlIXu1ko5h0EQ4JLaJdF4ODZz38w+rLZWrUBjtbWK+co8ivkirtt/XewY6S6UejOlZTaZDpdzNl2c\nxkprBVOFKTYoMY+3xGwmU1mpxHBDICuNuXWmlJVK5KAx0L+5gvnKPHZWduLcxrlo8cArK53AkIYW\nESJwmII55Bg2fdw2t1Kp1C8JgMpUVso8Q1hJtWchSpxzJ5BVShgWr4RXIM2OtuVhDkkxsjCl5PDc\nM10KjkWyUt9xlDoVexaGxMyhZfFEcl7N/E7u+cgx9NQyYw4tJmN6i/Lomede5c0V/MEX/4AFmZIF\nDddCpeTat6kAACD+1NFaEATPMn51x/BfAER6nmcC+IxrOxdak7h16oE/FfmuVnn26LLLRuCwUlFm\nKbZG4NAHjlzg8IorVBC6c6cs+E/DHLbbozH+6Z8Cj388X1OxVlNg7HvfU4ydjzmk7yR5iTZwuLCg\nAnLOAGVpCfiRHxn/OylzqI9JAiJs4NA1NylzyIEafV9kyJNWMsmNi/aXRlZKLStwyMlqyWxmcVFd\nBy4pMMl9zVIvUgBNwO/4cZ451KWnKyujHFeOOWz1Wnjs3sc6mcNivhi9mC+dvZSVHs5NzSnmsKeY\nQxOw+VgI6pMP8qiWqlhrr7HsGvfSjq2Me1Z9uUa5Fy5ZKfci1cezb2YfHlh5IHqx6XNr99vYP7N/\nTFZq9lnZVMyhbW660Yg5N1/QYss5my5O48GVByPZGCcZjslKjeCHlZUKc5Mk8jMJC+HLTYq5dXLB\nn0BWKpGNmed1tbWK+al5LFQWxq5rM9jyBZHSRosIb37Wm3H1wtVqbgaAtjraCqS3IjZLOx/Wcy/o\n4wv89W0FucApK9VBrWTxyJt3lpVcWujo6TOtkUg4Af5425hDypWeKkxFTsvUspqbs9yFds2mlbBK\npNAS8JN0X4DlWHvAepalLMznldkG4UCVVTKu/Wa3CQD4lzP/Em2HDNSCILAyfjGlR8oyLoA6Dh3w\nJTac4BDAeyy/p7CdQOIVnu1cUC0pu0YsxOHDvKz00ksVKGm1RjJDW4BNDpeTMIdUquDaa1XgmpQ5\nlOQckvviYx8L3HuvYms45rBa3V7mkAxHAMWgmWBkeXn0PbU0zGFSee6+fepzdnZrZKX6vmZmlHx2\nejrdmLkx6POXykq5flkY0rjmf8klwN13w5lzCIzAoXTxZBJDmqNH7bLSMAzR6rVw+Y7LcQfuUIY0\nTM5hIVfAUlPVw+Dq83UHXdRKNTS7TWx0NtiSBxzrxb4kc3lUi1Wst9dZYxvbSiw59gHpmMN+qAxp\nfDmH5cK49FQPoOcrCthx+ZTtXhuH5g6NyUrNPiutFeyY2mE9Rq6cQy+7Zgl+povTWN5cVuAwK1mp\nMDcpthKfkWmNb/7U1+wnKWXBBdq+HFiSC1OgPVue5RnIjGWlb7j+DeNz09iTXGBxtDUZDUc+pZjN\nyqDGpbPO4fDezoU5L4AEZKxp1I9ZiKIFtKyua8n8bcdaEoyz977n+UjPax0cmvcigMkkwx4AncrY\nSCBP9pn/APbFo6TPK+v7yqF0yTLnUJJOwTGH5F764MqDqBQrCIIgOo6FoGCVlZrXPreYaZ4zDqwD\nE4DDMAwvd33/cG3SAJEC7euU+giPeAQvK52eVizeyZOjPDhbgN3pKEDlA4d6oE7joP/n8wqw7dkD\n/Nf/6g/Y0zCHVNj+7W8HfuEXVHkIExySIQuBQy6INltWzCEgB4dS5tBnSOMCkFQ+olTKRlbK1Tk0\nWUoJOJTIZQGZrFTKHLrm/xu/oVi2N73J3odbZNDB4a23jphD23hsBe4lcmGdFTx1SgG/mRl+PHNz\n7pzD7qCLXKBMBwAFbji30mKuiJf9yMuwb2Yfy670Bj2UC2WU8iWstFainEMz0JTISok5rLfrqBTj\ntbXYwE4oPfXlXpQLZTQ3m9Y+/bDP1rmjfemsnzm3Vq+FfVXFLOrMod6HykvoTQ9sbDmHZvAjMWSh\n4IcMaYg59BnSmOdjUlmpz2lRyi76ctP0uVnZA0eeSyQrZfIpffMnWSkF2guVhRjIZI0rJpCVusx2\nrMXrDTBq5kKZY5LkwU1iJCIBPjRun6zUZ0oiZg6DPNphOxqThBXLoo/rmvVtJ83zkZ41ZI5lupXq\nZlu+uWUGoD2LDFJZqe0Y+WTn5vNKwppyzqgsEDWk+1nUOcwF8VzBWL9BvDwToOrgAspM65qd16hx\nDY8jLTRJ7v20qgEALLCMtmH95ge4SYw79D6PfjTwUz+lAIDZjwDSvn0jNoeT1oWhCpql4JAri6GP\n6VGPUoA0jVuphGE6cwZ4wQuA5zxHgbD19bghDTGQBA4JKLvAoaTchw6M9dbvj3IOgVGZCn1My8sj\nGbBt/u32CDzY+iQF2Y95jPosFGTA13eMbDmHwMj8xjzWNkY4K7dSCQPtk5X+yZ+oBQdXs4HjXE6x\n5g8+aDdjovE8//lKfi010eHujyc+UeXbHjmigCIwWrAw2cXmEO+Y4JBKPpxrngMATBen+TqH+SLm\npubw4ke9mGVXSO5YKVbQ6XfYgsmcJDImrdPy4CaSlQqZwzd//s14yz+/JfobnyHNIBywdQ4pgNaB\nnTmmdr+N/bX9WG2tjhnScOY2Y+PWjqMt5zBpgKSPe7qgVqIo51CfPyc/NM+HTVbqA5Dm3CQyLQkz\nYmOq9LlxuXTmdsw2xhwmlIMS6N9ZUS+H+co8CzJ917W0cfeaFByb92wWhjRpGR/JdR2NaeB3K9WB\nFnefSZnDJCyUhBWUgqO0CyOxhQgJczhcFDxQU1Iw061UeqwnlZUm6pOlrHSCnMMk5ywaU0bM4dHV\no7jpn26KGDnzOWM2YvfMPp1+B6W8CkRnyyrA0K8R27VvKjS858MC1gGwz2hqP5TgMKnZSD6vipcH\nAc8cEjh84IFRXqIZIL/lLYplaLeTgcOOxvhKQK10br4Audkcz6MDVM4hd4x271bMYqsVZ1jMJpGV\n2voQc3j4sPqZYw5bLTVOvUmY00mZwyuvVMDBJ6uk887lpunz9MlKAXV+fKCf3oe6S6wv51ACIG39\nJDmHvu9d85+dHbHYrvN6003qfpSCfg70XnmlWhhaXVXH3DxGErdSAodveOYb8N4XvxfFXDHOHBqA\nhFvVJLkj1RYs5UuxlyT3ouAAJCsr9azoip0fjeDvzf/8ZvzeZ34vAkGlfMlfyoKpc0gvyePrx8eO\nkz6mdq+NfTP7sNJaGbFQFlmt3iTsgUiiZVkZny6OwKHEsc88Hxxbw0kvswiQJXXcJLJSLucMkMtK\nfaDOlm97+bwSOxFzaJ573/0hbayEW2DKwbH9rrlJ8uCk51XEwjhYQZ9baQzUegxZXP0G4SB6ZkzC\ndicCUAJnUEkf67zMnMOhIc31h64HgJgDte9YJzr/AoYpK1mp2JAmZc6hBBz6zofLGMvXPn3k07jl\ni7eM5Rz6ZKVcqkin38G+GZWPVCvVonFLGFjf/KnMk012S+0iODRaUrMRVz8ChwsLSlY6Pa0Aggls\nbrtNAa5ORwWakzKH1NKUYEgKjncMU3RMQxrqs2OHykms1xVgmVRW6gOHhw6pn8mJNKlkVtonjTyX\nc33VG82bc7XUt2srnQAkk5XSmHz5hPr8JQDS1k8CDn35pi5ZabWq7qFKRcZkShdPbMeRnG9N11sO\nHFYqPDisFCq4eufVeMVjX4Fivmh1K6XGvbiIXbxun9K4U36CuVLvlddostL19rrKmRACqKQr4wCi\nlx8FOsV8HBzrjTOk0dmVdzzvHfjSr3yJHRO5la5sjhvS+CSzEgmOJBfGtjI+Bg4Z0xqfFJgL7GPS\nU5es1OMyKmIGEhryuNgDr6yUuR59slIK/i7focAhJ7v2BaNJGisrFc7flLq6zr+NgU0qK51Enknj\n9spKTVDLgF7uuWZjc2nMWdQ5lCyeiMxvBH1ofyzbzTCHtXINZ37rDGbLs/HzKnCGlcjFJczxRItH\nggUNEXOY8LzazFa4575ZyiItc0g560fXjkbvT9szhMAZV8qiO+hG7waSEEueIeZ5ZcFhEEQA0cZA\nAhfBYaxJS1lIAmQChzMzysTFJiulbSdlDpOCw6TAj5oZaBMQA0ZOjDt28BI9cpE8dYoHh2EIvPCF\nqn8WzCHVqFxYiB9ridnOVjCHep8smMNi0T4eXVa6FfmEUlkpBzQlhjRpmEMaN829UnGDOm5etjG7\nmHWSMJs1TjlwSDUo9UbMITXupcTlnMWYwyFTQavMwPhLQmJ5D4xeyAdmDuDI6hFeVmoJotMwh/Qi\npf36Cu8OwrghjT6eKxeuxNMOPi3a3xhzOHQrXd5cHpeVMnmAY+MWmK1IQJYN+FH9ykqhEi93YQHZ\nJnOWWlYqlGlJ3O98DINpyGJlDm2yUsQNaciUwxdo07Eu5ot4xWNfgasWrhK7GqaWlRq5qRLmkDOk\nYWWlHgbWNKQRMT4SAJGRrJQz2mGZQ4dKwcV4JGZFJzxGSRZGonlxi2cMcwgAe2f2js3dNebY3CRm\nM8JjlEXu6sTMYYKcQxvw8TH0k4DDjc4GAGCpueSVldLCDffc6/Q70aJwvV0HEJeM+u59yT3ruo+4\n/Pro763f/AA3LkA05XcuhkUPJKkQerWqwKFNVkruiefObS04lLKiSQx5CIzZmENA5R0eP86Dw1YL\n+NjHlOV/kpxDGzgE1Hk6eDA9K5iWOcwKHPqYQ67APY2HZLOuPrZxZ9UHsANoHzNoGg2ZjZhTDrAR\na2qr8ZmGOXQx62RclMuNn1vdtIbA4a5dwMbG+LZNcMgBqO6AYQ51WWEYRkHk8696/mhbWvDrAoec\nAcajdj8KZxpnrG6lvsBGyhzSiuggVPkZHHOqN3J2M2WlvtqDgJKV7p3Zi83eJlY2VyIZp4g59Kyw\niwJEh1spMJKVxiSzjgAJ4AMyTnopCey8sjkB8HUxp1HOYUpDGtOFViKtivY3vB7f++L3xlgY2pZP\nniptnKttGubQBqIyl5UK3SonlZXq503CHLpUCq5cqaSsqHTRw5uXOaGsVO9jKkbYMWckK5U+H3zA\nz8VA+tis7cw59BkETQIOG50GAKDeqVtlpWEY4kvHvxQxm9y7Uc85XN5cjsaty3NzDESTAGja1iAc\nWNlV4CJzGGsu+Z3JDJjNJiv1MYdkWHHmzNbmHKZlxVzsGgXcrj67dgEnTvA5h6ur6vPcudF3aWWl\naVjRreojARpmk4Bjrq6ePh4Ch5Jzb45bwognkZUmzTmUlPIgcMjlHBJYK5fTn9ckoH/XrvG5uWSl\nu3cLwKHFrVJfwTMDaHoB5oIcrtt/HQb/RQ3CDMYkL0kKRvXabFxgI5Xxje3LYjhBn7kgh0KukDjn\n0MqeGHPr9Dso58uYKc0gRIhqqRobt6+u3ESlHMyV8YxkpVxARn2IWZO6laZlT2KGND5ZqY09COwS\nLP06TxL4R/vzsN20QGH2yUxWauRc2oyNfAZJ+pgkuYtSWWlayaA+N5/UUb/fUzOHuRFzyAFjc1/i\nhZFJZKUCabYE+EjufR9YjfWbIJ9UJD3V7tlJZKVShUoWOYfsvrTz4ZK3+9pGV73k6+26VVZ63/n7\n8Iy/ega+evKr0TuWYw7JO4CugzH1AbMAC8iAODA6Bq776CI4NJpENicJtIE4c+gCh1NTChxmnXOY\nlRzS1kf/tDE1u3fbmcO1NfWpg8NJZKXmuCWSWQmIkDhxJpUomo3Opw8c2nLugHE2yzcvc9zSnMO0\nslIfOOTGZzaaP8fm0f7C0H092sYszTmk7Tz96eP99MWjXG6kCFhd5ZnDTr8zDvw45tBgIUwJiml+\nQS9wM0ASB9G5PPZU90Tj4dhFryGNkDnc7Cmnno3uhlhWajq7cQF0NH8m2NKDF9v8ze1Igg3v6rnN\nrbQ47lbqZWDNfEImINNzSqiPjxmaiF1MyAzYAkROXtXqtcYki9LAN0n+Eo2Jk5WmYQ65wF5n/KXg\n2LdY4WJgswI1vkUPvZ+EqQAyYA4HfSfjkZTxmuT+SJpvS/18i2ecIsI8r1awnlAuLnIzdshK9UWo\nzGSlnmvEyfZ6pNC+xaNSvoR2vx3NK0kzmUNOfbDSWgEAnN0462UOj7zuCD77S59V8zeesxLVgAQc\nXmQOhU1q3CFhDgmwzMwoqaBNVtpsqhptx48r90+f9I7YIx843CojFR2ISZnDXo8Hh1kxh/2+Hxxu\nBTimJs3dnBQc2mSlJjiUgCMad5KcQ4mslJ6pWwEOaf4uxo9+3oqcQ/0Y3Xijcj0156bva3YWWFzk\nwaFpec+9JExZqbmqydnmUz9RgMhIHXdXldMOyUolACr2Yhcwh5SfsdZSZTMKuYLXkKaYL8YCaJtE\njxs35fi55h+bm09aFuQxgJ9d5EA2jXG2PMueWxdzRGNKy/iZK/FZmdbYSln4cmA5Wemut+3C629/\nfRTESAJf89z7WBjalg2IJG1sKQuhrNaVC0VjcoGsaNwCxicJgLBtJxp3Elkps6CTJufQZqQhMVJJ\nam4idfQUyUotCxHm88Gl0JCAdeeYMpJoShhoiRTafD9w+5MwueZ59d0f0Zi0fsV8Ebkg51Sx2JoO\nDqPFVeMZstpSQW+9XbfmJRI4PDR3CJfMXhLNTQfZvkU4Hzj0ybMv1jk0mtTOX8IcUj/dQZIz5SBw\neOyYAofdrjv3qtdT8sEsDGmkjI8t0KZngasPye9czOHSkkxWOSlz+HAwpEnKHOrAT2cOs3IrlQJI\nkzkzG+fUq7ck4NA1N9q/RC49CSMcBKpeormtJOBQD5LoIe0KbM2cQy6/ibaVNIim1erd0woczk3N\n8flbjjyoqI+AOeyHfUwVprDWXkMuyKGYT8EcOnIO9fnT737/x38fz73iufy4fYY0EsdCQSkHfV9P\n2P8E/MrjfwU7p3fyq+cCOWTafClRsCUEY14WQgN+SWSlG90NHFs/NiYrTSItA4TXLBMkuWSursaW\nssiNB3a2oNV3bZvyTFu9yESSSUF9uixkpWOgViI7dyxESVgRGndaVjQp8JEoC2zzkuTcSUB/bG4T\nykqTSE8lzqiTGNLEcpvTGgQJckArhQo2u5uxv/U1WvCst+tWVnCtpYLeekdJTzkAqeccRuM285Y9\n6RTOBR2BsdOHfu5D1nn+UIJDibTOxRxyQINcFMny3gyQdXA4N+eXH/ryzqhNyozY+uj7evnLgZ/7\nOX47tC8ChzMz8bkTc7i5aQd+epMY0lDbbkOapPJUs2XBHF6iFplYEyUfOJTmHEqkp5L7g/ve1cJQ\n/b3p1mrO7dSpbK99ieurizlsNGTgEOBXUMfyIXJ+dgmIs0KSIDqqczhk167bd50osGH7CHMOd0zt\nwGprNZKV+gxpCrlCfMwWeQ0nY/zFx/0iPvmLn2TnLzKk8eQcuoJIzvzn2t3X4j0veo/qw5nWOJgj\nQJgHOaFbaVJZqQ9ASuSQNG4AmCnNjJhDIePjk6hxINsHIKn904P/hE6/E/s9Net9ncKQxiUttK36\nJwUHElbIZ0gTsXmuPECNORYtHjlAfRJDmrQgwjyOaftIF9iSyPddYD3N3FJLbwXg2GSzJMyhTwqe\nVf1KgH/OVoqVKO0hSdvsbaJarEbAj2MFdebQJSt1OR7bjqPkvU/96D6yMfBXzF9hnecPJTiUSusk\nslITHM7PxwPkMFTA6NAhBQpmZlTwO0neGTdm29ykLpu2PjfcAHzgA+4+VA+OM6ShAuHN5ui7rJjD\ntKxgGhDxr8kc6vv6yZ9Uiww07qwlo1J2UeLmy33vaoOBAr2Fgp3xu+yy0VyzYM2lDKwNHM/Oqk8x\nOPTIi8y8LM42n7aTRDZF+6IXzudf+Xk85ZKn8PmEHjZLMi/qNz81j7WWYg59stJBOIgVDLayJ54c\nJxq3vurNvUwTByQOuVNMMmgcR5MV9oED6ZhcAaKP8Uw6N2cpiwRGKgCiRZAzjTNjzGEWwTgnLeNk\nbJys9LnvfS4++O0Pxn6vjzsNc8jKSj2LFb6gXnLvS/r4Sif42DxfrmQS5jAJEJ3kupYCDcm+kuTT\nAXaZs288XL+0uXlZLh7p594GMn3geCuua+pn7mu6OI1mtxn7W2rtHp+T2Bv0MDc1FwE/7hkSgcPO\nSFbKeQ04mUOHrNQH1gENHFoWqnzthxIcZiWtC0P1L5cbgSMOHLZaigU8cED9XKvJwCFn55+GOUzD\niiUFYsQc1mpxcEAgT8oc9vsKIKSRlWaZc5iGOfSBI6lbqYs5DAJVxoPGlIVkVMIKSmSlk4JDyit1\nHeu77wa+8hU5cyhZPPFJk81++nioDuhEzKH28Dbz0jgJG5BgZdgStFx/+HoEQcC+SF0ACuBXYqmP\n/kLth33MlGbQ6DSQD/KpZKU2t9IxKZODqTFZGJvzJ+1/kkDLm5eZS17KQhTYC3KhJp2bNCALw1DM\nnLX7quDryfWTY+AwCaAFRoyzbe7Ux2daozdXLqK1zqF2PdrkoBIQkYQRzsqJ03buo3EP5FJPG+gL\nEY4/Hxyg3mVIkzSfcJI+kn35nukA/3xwLVbY9mWOW2Q2I2ThJnE8lpgxee9HocQ/CSMK8O80n6z0\nlz76S7j9gdtjv+8Nepgtz2K9va6AH7MoWu+ouoWNTkOxiw5Dmknm5rsf+4O+kzl0tR9acJhF8Et9\nggDYq2qYsuCw2VT5g/v3q5+zBodZ9EkDoPRAe48yP8TjHhcHBzRPHRz6mEPTjAfgS1lciDmHWclK\nfeMxxz3JdS1lBX0Aksu3NbfhanSOXWze/LxajEkjGZUsnkgAtL4dYg4XFkYseTQfCXNoBAnm9zZD\nmlggkRXDktKQhhw0zf3RCm0+l/eWsuAMaWw5hybw8+V42QCLSFpl5ngJAkTWlINjF1PKSk1WVBJI\n+fJzROyJZTu00EAmCBLmjKSbJ9ZPRIGOFIjGTIs8UmguYDe3I23WOofD/VlNlHL+a0TMCCc499bn\ng+C61vv5HERdsmL9+qBxu5h8jlnj5pZVzqFUeil9znLz8pljSXNApddIkvtoUkdXn6TcvB/TSvxF\n17VxPlhw6JGVrrZWcW7jXOz3vUEPc+W5MbdSjhWcLc86+3T6HZRyBnMoMJsxJf7WBZRhv4vMYYIm\nDf4kfYjJIuZwbi4OWKjcRRbMYZrAVtInLcik8TzhCWrMZq4YzQVQgTP1cc19MIgzZ7SdtKUstjPn\nUCIrdTGnNuZwkjzZ7ZKecvm25veuRnPYKsloWgBp9tOPda02+oxd+ylyDmOlLCyGNJLAzgQaEhlj\nWkMa6mcG7dVSFc1uE7kgl6qURdKcQ2sfB2BJJM8UFIqPxm3sz5SV2gDkGDh0GOAkAhGCa0TCjLjk\nbtRPYqQCqABpT3UP+mE/Mi1KCmgB+TUbC9hz8aBN0rw5h0JDHh97JD73EzhxSsEIyUptgM2UeVuZ\nU8ONkWPyk7CUk4CIpEBjIlkppyxwLMJlIStNch9JjpFIVio1pNnGnMMB4teST1ba6XeimoZ6I+Zw\nrbWGYr7I5i1HAJLcSpk+bM6hmdstYLJt1wjFEWmZQ3uRix/gljT4M/vo0kjqMzWsc12pxNkTAjVP\neQrwtrcBj3lMPCA1G2dIkzawTWOkkgZkUVDPgcNqdZRzaIJes/X7PMjUwXiScW83uzppziHHHLqu\nRx/jJwV+tD+paY3PtIVrloXpqLlkpVslGZUa0uj9SE4OjO79UomRQguMGcyg1ZSVulgx6jdJPThO\nopfGkIbG3h/0gfyoX7WowCHJSp2lLBhDGtcKqiQgETGHHgAlAZCs4UJaWal2fUiDf8m4bfP3BpGC\noAUYgV8noNdlpb02yvkyZmuzOLl+MgKHSY2WxIY0CWSlVEOSa9Y6hwJDniTskfjcZ1CmwAn6cwll\npYLFI59pkc+QJotc2rFnaEaLJ7a5SaSnkn3p/Uiim0m+8QSOx/o965SUS5jDjHIOzWdojDn0yErb\n/XZUtkJvUc5hp45qscqqD7oDxRw2Oo3YM03fjmvhWFJaxfYuAtTzqDfoXWQOk7RJJHoupubee4Fr\nromzJwQOi0Xgd35n5Gjqy03LypBGwrCYfSQ5h7Z9ceBwdnYkKy2X/TmH5txtY9qqnEMJ0JDOv98H\nzp9X/+92ecms3nw5h+aYJAsaSXMOt8qQxgcOacFFcs6ykoxKFgbMfmScA6gSLQAvqbXVX4sFrdrD\n22SXXIGdN1dMaDYjeWn7gnFgPPgLwxCDcBCt0OaCnFNWSoGOyZxOEpCMBayCXJhJJGo2t1JzX6as\n1Hd9SF0UJdJCr2xOwFT5CqH3Br1EstJSvoRaqaYcbYNx9z9n8CeQZ4pYOiZoA+BcxDBrk+pzp31J\nmEPunJhSPwmT7TtGkuPoAiO61FMCDl21SX1M/hhLaQlqkwLoSWSV0vPhez76FgWB+H3mk5XSAoZo\nQSsD4Cd5z9jYZRFzmFHOofneY8GhR1ba6XeishV66w16mC2pXJLp4rRVVkoAkhxNWTMmR16qZFHU\ntaATgcOLOYfylhVzaPZ51KPUJyd19IEavZGdf1aGNNLcrDTMGReMc6CuVhuBQw746Y2YQ05Wul05\nhxKgIekDKKfX3bvVee124/UrzUYAMimbJQWQk0hGJX0mZQ4p5zDpNSth1jlQKzmvZr8wHM3lZ34G\n+N3f5Ws89gd9FAI3QOByDnV2KaucGtqXjz2xOu15nObMudHLrVKoRDmHLlkpHQcuX8QX2NhkbCab\nkbZeZCxAErAHtjy4EKGTYYmBwwlWkM0+vmBDDCI8xiVSQ5pOv4NyoYxqqYqlzSVUipU48BHkFHFg\nhAsQfdJTABGb4AocU5eyMJnDlNI6qdSRDGBExjZCWamTzfPJaiXMYUIgmmmdwwlycnWzHfZa45QF\nDoWGpKakNC9Ram4ikdX6ng82xlfMHCbMOZS891Ixhz03cwgMwaFDVkpGFTw4mgAAIABJREFUbJwh\nDffsl+QcmsfIBw5dfVzthxYcTpJzKGEXOVmpqw83Pkk+XRZyUGmftG6dxBw2m3wupdlsANIHsnX3\n2KRz26q8TNo2oMpP9HqqiH3SfFOdqbLtTwIgt7qPz5BGKitNA8QlktEsZaU0l5e8BLjlFh4cSgxp\nzJVWkzmTvLQlAMo2Hh+TyfWRBH8UsJcL5UhW6iplQS8xk82xyeb0oE3iaCrJXZyEOWNXxhlTDj0A\nYEGNEBxLgrYx0x6LmYhoJV5nhF3GJWSCIGQO2/02SvkSqsUqBuEAU4WpxHlHgD/Qts2fYw4pD8kV\nOEpKWUjcStNKCyVBNBlE0WJE2nwyfW6uQNO81iZiDgdut1Lpgo4X+CUEPrbzYbo+W/NbPQtsEuYs\nGlPYF+clSoFvWnZRKheW5ByKwJFgUcwHDot5vubusbVjANw5h3PlETjkniEkK43qHDLMoW2xSsLS\n+o4RcFFWmqpNysL4io5PCg6pvxQcpmEF07BrUpOQSZnDwYDPOfSxPjQv8/mWNOeSQCa3nTQ5h81h\nzvPRowoUSsGhj/EyxzQJK2iCTF8fF7vmkgz7mu5WmoWkOitHU7OfbRFCBA6ZFdQx5nAYQNIq9MRO\ne56AhOvDBTZjwXiC4G+qMIVmb2hIY3khA6OXZcyQRbDq7WMh9PG4+kzCnHHn1VafUWdYWDmkIavN\nqlSBLRiXBJH6/G3BRmSCIDgfwEhWWi1VAQBThanE4AjwB9qAPxilRoyhy6zCxxzaFisk0kKTOZXc\n+16GSehC6wN+vjxA772WIOfQdr3Gxj3B3GL3EBMSJwVsgOxa4wJ7yb1I26Jj5Dr3vgWdzNjFhM9Z\nwP4uyoo19x3rYi6e//65hz6Hw+84jI3OBtr9ttOQBrDLSnVH03zOwhwy99JYzqHjfBBLLQKHF2Wl\n8pZlgDwJOLQZ0ujgcCvyriSgRlrnkAOZwPj+ej1gZkbVe5TmHHKyUu6cJM0TtfXTt0Pg33yWpjWk\naQyVCcePJ5OV6tvhwKo5JmmunIQ198kqt9KQhss5TAvqpOyiCSAlOZfmXKQ5hyxzqL0kzVXoiQCL\nJEA2+nAv3BgLkyD4K+fL2Ohs+GWlw0CZW/VN65BnruZ7DWmkdeUs50OXllkBgg6gJ5WV+laZDXbZ\nG0QLjW1cIMIVkJigrtPvoJwvo1pU4LBSqMjOByOFljDivkUPYMQYusBht9/lnQY9ixUUsOljct2P\nk0gdgdExkMjvXGCEzolrYcBcrJG4lbquESkQlTwfJMoKcR+PpBqwL1b4mDPJvaiPySUrTZpL7bzW\nIGdXnc9Zn6RaYHwVe+55zj31Y8GhsVD59dNfB6AUDZ1+J7WstDsY5hySWymXc8jJSgU5h3rJKNfi\nwEXmMEWTBshp+9jcSvXGyQ/N/lmxeVL2RKtNKwI+ScBxtQq02zLm0NbHxhwmAfSuuXGSwUm2Q80E\nh9PT/lIWJnPIMVU0pqTXrAT4SGSlk+QchhYjQNe1nzaXNqlhk+v8u64RsazUk3NIfXxOpKnkdwLm\nkHvhSLZjzo3mPlWYEstK87l8nDkTgCNJQCIBkCL5neV8mHXcOFkpMG44xMlKxeA4oSTMFmxIg0j9\nWFvB4TC44a776HtdVtobyUoBxRxKwJF5jKx1DnV5MjM3ThJGzGGr12LnSPtlV/19OXeCe1/K+Ehy\nQHVwmPYaAkbnzbUwEGMOPdJCSc6h1ZAmkC16JHk+ihhIzzFyLVZJcu4k96I+7kllpVmxi6YaQsqa\np5l/LsiNVDUC0As4ZKXGu+j42nEAatGq3WvbDWk05pB7hlCdw3a/jVwQz6MH+HvEVGj4rjXXNXKR\nOUzRpPK7tNJTU1621bLSrcrN4vYFjAJ7KXtEeXZbDQ6zZA59zJlkO9Q2NpSsdmnJLysdDHhZrSvn\nMKnLaFrgJ5WV2hhxYFySzTUbcygB4lnlHLqAuOsaySrnEBgHEJIAWSy/YxgWTsZp7i9mSCNhDod9\nKOcwFyhZKcccvuQDL8Gt993KvkgnkjLpAZtHxgZ4gnHP+aAxedmT3LirpUueCGQnK7UxMVL2YExW\nagk2JLJSkzl0yUol4AjwB5o0bg7QmUFbp98BAKv8WQJ8pXlXHACS5rfqfSQMk0RWKZExuhYGaFsi\n1tyTcygFopOwYmLmTN+XJWw2nxE+5pDrk4TJ9TFHkvtIIpmVLERIF6GSGPJMcs5E4JBhDo+tHwOg\nFq1czOGls5dGc6LrI9RWvPW8xGjBU2JIY+R2u65/n0HUpMzhD22dw0lyDpOY1gDbDw6zMFLxSRSJ\nHZWwR8QcdjojWaWPOSsOFTs6Q5MWHCZl/CTmL67tcLLSgweB5WW/rJTmnobNkl6zWQA/F8h0AX86\n79w9QfuWsubSnENfnyAYzzOVAnFOdtzrjZ+r1MyhBiAmyanh8jw4pmIMHFpkpUmZQ+pDzGExX7SW\nsrj1vlsxU5pBPoi/SCVW/S7m0Acg9e1MwtIC4y/37ZCVJglsbUzM2PwdQaTEIc8nKzWBf+RWSrJS\nxq3UB44Af6BpG7fZh8YE2EtZcJJSYHxBR8wcenIORUy2UFaathZe1M8TjI7dawJZrW+xJktDmkly\nN2Pnw1Pug+bGMssS1thzL+rj9uZAJshdtT5DhMZXSRdGvIY8GbG9NDeznynxBoDV1iqAIXPoyDms\nlWoAgOXN5THjJzp+JCulsdgMaWLlcIRmM6LFGs+z2Nd+KMHhJPmEFzo49Jm22LbD9eGCd6n01pz/\n7KxiDrl8OrOZ0kIah8SQRjIeX+6iJL/Ptj8bODx0CFhZ8buV2mSVEqmr5JqVSKonMb/xMYc07253\nVDxeb3qdQ30BQQr8JGZE5riDYAQK83nZ+bcZFunbAdLlHALjjqVWSVgK+Z0kQOb2JwnGzbkRK1bO\nK+ZwvjDPmgCQdOfS2UujF6kEHElzDqUytmjugpIQrgBxjBkRyEoluXK+oHUSWalINiaQ8QGj8y/J\n7wRGbqUUSJlupdLrmmNpJQw0ZxSRmjnUpOCJmENHzuEk+aZRv4Hd1VKSl0f9uv2uF7D5JNU+MyZ9\nOy6GWgJqpcoK37GOgSPPsaa5uVghwM4cSZhcH+g3tyVdZJhEVupVcUiZwwwUMxLVACcrpfvXl3NY\nyBXwnf/4HRyeOzwa96CPXF7tg2SlNBabIc1UMB4ESRfhJOz6xZzDFG2SfMIsDWkkOYdpDGkkOYc+\naR1XU9DcVhJZKeUc9noKFPhkpRw49oE6iWQyDPl+ZuCfRe4itY0N4JJLRsyhCxx2u7wZkUTqKpm/\nNOdQYtjk2xfXCPD5mNO0OYe+hREJOJbmHHLHKJZvnMKtlPr4cg7FK6hG/p4P+HFBibkdac5hPpfH\ndHFaubYFeVZWeqp+CgAi6SkLjjxBwqTMYaIA0VMEXgejElmpz2VTUuvPJwkjydMkiwzSFe3+wFHK\nwrj2O/0Oirki9lb3AlBSrzFGXCDzBWS5m9y53SpZqQtAStijpFJHH8MkXRiYSFZqMGdpWHNAkyY7\nDGmSms1Mwr5LwBGNybUQxYIjxzUrYYSd7CKSvR8mledK8r/NWpCu+1EilZ8o55CRlRJYpAVLW85h\nIVfAI3c9EpViRY3bAH+6rJTeaTQOauzigJlz6AD+YlnpxZxDeZMG0ZOY1kjAoS2IzrqUhQRoSECm\nuS0Xe2TmXBI4JMbIJyvlwLFEVuqTuRK75GKYbLJCDmj4AAvNZ+9exRz6ZLU2cCSROmZlNiN1RpUA\naG5+pVIcHDYawHXXAfW6+l4iq5X0kVxDZj+J+Y/tfJh5h5y0MMucQx+bFQuQLaYcviA6xtQkcCut\nlqpY2VxBuVCOtqsHSQQO19vrvCGNMJ/OFoxKAGRihzxBzqFEVmo1UhEa8kglYVLnR4ms1LUtum6T\nSMsKuQJ2Tu8EMKwDKQS9vutRIi0zjzX+f/beNda27KwOHHOv/Tzn3EfVrSpXlcuU3xgwsc2jI5zg\ntsMPAhaYYCQgSiOgaVBCxx1CWoqgAgY66qAkihIlIt3u0Oqm86CTJiQSHaSkwVJES50A5mE7zbN5\n+IHLdW/de+45Z5/9XP1jnW+dudde8/vGXHOefc+teyZCt3z3vGvNudZca31jjvGND+fgUP5sNhX4\nRZpytKkGmsEvwxpbIIoFUFZZhLbxth2LZQ416Sl7LiYnlwV+Xd0xgca7JrAem+DIZM4SZaUxDBsD\n/Kx6qmVZBtd+Wy3I1rVvKWYizIg0INrGHC7WCzg43J/fB4CgrNTa8F2sFxj3x/V/t/UJbQxawNe/\nBtq36Io57NCYwDYFQOZyK/VBTUyBdwv4heR3DDhkmdMmC+mDw8mEk5W2zb8NIMSMh+mT6lbaBg6f\nfPKcOdSYU405TMk5tAB0F3DURVa6XLbnXH7848Cv/Arwb/5NJTtuk9V2MZJhDGna5tbFrRTYBodB\nw5EMOYeF8yzGydysto9JWxC9xS42DWmYnMOzIGF/sI/pclrnlTVrHfrgsOcChjREgfeuzGEsGGGK\nwNfnIxwbk2SlpNOgFWgz0tsNQ5qQi2TvPM8lBKC2QJ0rauawOX+GOQuNmzKkCchKh8UwnHO4Xmzl\nCjWPxVxH6aeyJ6wc1JAfprAw9dxi3EoTmENRTdDnYhQBhNmK+p71n31CVtr2jGy5GVv3PlFWurXJ\n0FFWyrB5ft4d/a4JSG9zsL3+eKQfyxzuD/dxND/CwfAAR/OjDaMZIJwq4r9r/D6z5ay1D8Mcautf\nk4sD3kbdFXPIN0ZaxgSRGtDI7VYq42GYEYtdZAu8WzmHzHWU+YiUcj5vB0c///PAW96yPf9mMK6N\nOwXQWk6UbfNiweFqBTzxxDlzqOVchpjDXKUsWMmoBY5Z6W3b/NrA4W/8RvXnZz7Tzhwy+YRsH2vc\nKbLSrWe/Y87hlrykY/2tNlawDfhZMkaGXWzOTeYuTpTyZzNAvj29jX6vXzGHbYY0gYDMD8YYs5mQ\nzHNrt5pxLFR2dFnZXD1uSw6Zwa2UlQO2sRlyHKbwssw/xJo2mTq5Rm9/+u34xf/qF7f6MHlQQPsm\nTPNcbc9R8zhABQ73B/udcg6t9cioBpj1yDBefj/tOHIMixGX41gBK5CHOWTzG0NghGHOeq5Xb7Ax\nCo0YWWkIaGrXSMZYlmWyrDTa3CVBVir9VuuVet8s4xoBdNp6bAJaljllmcOD4QGO58cY98cY9AaY\nrWYbfUJ54v5z7ZtWyXlZJYO1ZoFNRYAGDq+Yw8iWmnfFgJEmOEoFh8x4QnNj5ZCxOYestFAAz3BY\n5d+1gcNf/dVzgNBmysIwVSzoZwBUzjqHqxVw82bFnIbmL60NGFtjilmzzHVkZKWs9LZtfm3g8JOf\nrP58+eVqnXTZ0OgqPW32Y4CvJivNkXPoO6mxu95MblZXWamfTwXobF7z4y+MofzZDJDnqzkenzx+\nzhy2gFWLPbGcD4H2vDQZs7V7zgTszbktS9u4JLg+GuDYYjQsUMuyMKF+zcLLVoBMy0rPAmTnHL74\n2S/emhez9oGwfNtaR20ugvPVHPvDfSxWC7w8fbl2MPTPFcw5jGUOA6qBGKmjxWYJiEgBGtQmQ/N5\nTMk5JAxpgOr6hMAIAyKYtUab9hiyUjnWxhpRTLS0c9XMUaKstAmyGUdbC7CEQL/00ZhDn12lwapx\njaQfzRyeKV0kJaJpSsPKSkVdIOeNlpUmugdf5Rx2aKyrYw6mBshjSMOwGaExsYG2BXyac4sFx6NR\nlVs2Gp0bw0h79aurP+fz7uCYAavMmFm30picw34feOwx4PSUk5UywCfn3GJlpV2Yw/W6+v/xeBsc\nTqu607hzJ5xzaG16dJWets2ti1sp0J5zyLgoanmJwcCfkENuAa2WQLILwxJTDHlvsAfAA4eNMS1W\niw1w2DwXE9gw0rIU+RmzCy9zi3GsDOXdsPNnQa3Fwljg0B83HZAQ4CjInEWsfYC/jhoQkzZfzXEw\nPMBivcAHfvYDeOxHH9uqYUYxhwQ4bmO9YkGNBaIY+eG6XOuGNL1zo6HgOkLjWUtlDpVzNedmPdcM\nC8W8Z8yAXWHFgG1FiLZZZd0P9r4CpKmVkm/MqCbkOoVAr8xNQK3F+FLvPYPJtZjDpjnacr3E/nAf\nJ4sT9Hv9mkVs9omRlcq1aMu37ior3cg5DIDjK+awQ9PyCWNkc6FabW3MWSpzyAS1MrcuQTQjmWzO\nLUZW2u9XQf/R0XkdvyZgBYBPf7qdPcsF1lNkpYzUMyQrLYoKHALdDGlYt9KLNFth+mjMoUiVB4N2\ncNjvn4NDVjIam0ubMjfmOrYa0iiGE0A7e+BLD3M67QHbjpWMO2Zb/pZVLF1knCInFZDYBKPz1RyP\njR/Dnemdugg6Y8jijykUsDeBRpCFYnbPCaOILcmoISsNGdJY96MekyF384NIDUD4Y7YkYYxsLMgc\ntQRIGpPNykpDEi3KkKYl53B/sF/VYCxGAIDfvP2b9e9MnUParTQw/1gzKpMVJIJ6a9ODWkcGK8Yy\nh1bOoT+30JplQMTWWutobAM0mFOGOTTGnYXtjfiGpMpK62efYA7lOJr6hHFYtcCx9u7v9/qtstL9\nQQUOJV+eYQ6b75E2WWksc2jObR1WaMj8rpjDyKa5WsYEvynMYTNo9VuIOWOkjgybxQbR1jWKkZX6\nzGG/vy2/E7BwfBwnK43NJ0yVlVpAgwGHWs5hyJAmxSSFBT5Mzl1MXl6zyX0NgcMnnqjA4WjEyUG7\nSk9z5Bxq7weTOWxjT1pyDmVXM2VHm/mQboGRgFspJXVs5BxuyEqHYVnpY5PH8NmTz9ZF0JuufmbO\nobJTv8GwGNJTVn5GyUqZenhthkVNsJ5gSuLvwrM77ExgZ7GLoQCxudZCAZLFZDLAj9nQsGSl4lb4\nOy//Tv27JivdYI075hx2kTpaICKVhWBkbFvgKJE51NZic0yhtWZt6FCqgeazb1wjOV/oWBY4lvea\ndT8s0L8lO+8ol2c2awBPVkowhxpgsVhR//vAbGjI3LaYQ8WQRpjD/eH+lmNpZ1kp+ezH5Ldq15F9\njkLtkQSHuRgWBowA+ZhDhqlh5Xc5guhYcDwaVeBPmEM/iJ6fuYafnMTJSnPlHDLgoIuMUc4p8weq\n+XcxpEmRg8bMjWEpu8hKNXB4enpu2sPmHMbcD2GmmU2fFOlpc9Oj9QNAsCc+C5GSe8GyMM2PliUr\nDZ2vzZRDGMNaZtMI2oU5XJdr7A32NvLbZP7W3BjJKB0gEdfRYs6ANFkpk98Zc28ZACHnSmGP6hp1\nBHMEtAfITUaUcSsNbmgYDHRIViqGNEfzIzw2fgyfOfpM/XuSrJTIOYxdj9a9ZfLXBGhYslJ2HaUw\nhwKyNYZajqUB32Yuceo7lAEjUc++AqCt++GDflZWSjGn1iYkwUBbG0w1gAz0qZls0hnVWtdAWFa6\nBQ7PDGlOFicoXJFFVqoxh20bWtYGg/SzriNj7KS1K3DotVhp2UXnHMYGrKFxMxJFVn7HgLHm+YQN\na4JDv08THBZFN1kpYyTS1ZCFYWBDpSz86xRiFz/4wcqYpWspixQ55EXKSr/u64B//+/P13VTegmc\nM4ezWdittMuaFYdbAYcsyM4lKw1+AIy8K5+FCAUAsQwDnZfYJittGNJQOYdnHy6Z2ycOPwFgO2hf\nrBd4bFzR6pP+pO5jsnmEIU0XZoBiYJXdcysP0r//qbJSdm5qrlgM0DAAgrCijGkLEGAOI4N6GXfb\nhoZVdLu5MQJsModH8yO88fE34jPH5+BwsV5whjSkrNaaPyNztoLIZOdLZ7PGW5s1CcyhrDNNDifn\nYwxpaFbMKIuggTFKVtrMOVQ2tCzJKNPHzN1lviGE8ZU/N+tdYzFeLJNtMafNd3Yrc9iUla7OZaX9\nXn9LVip1HK33iP/8yxpn8qT97yz7LraYw66y0paMuVd+YxmWnMwhU/LA798mq2RkfAzDEgqiS6+W\nIMscMoBVrpPISjVweHx8zrSxstIYsM7IUxnmTPrFyEoFoLT1+fVfB37oh4Bv+IbtuWtjijVSSZWn\ndill8a/+VTWnH/uxbUZY2nRa1YIE8rqV+v16vXRwzLiVboDD0AfgAeQcan0shqU1GDdyDn2A8Ft/\n8bdwa3Krnn+TOZTfhGWkJJPgDGliZFPBeZGmDG05l83mO9G2yUrbrnUQsBK5aTXbZ+zUy5gtKZMK\nRgxZKcscxrC9QPs98YN6YaLbNmramMOD4QHmq/k5OGwwh611Dgnm0L/3Mj9t/mzAbgE2i2FinS+Z\ngBWorpE8y81x5845tEAWQDJnBNBgZaWsakB7rtXxgANHDCsYDaBzyEot5lDJOfTHZL2LujCHtVtp\n74w59GSl8r5urQPckk/43V/63XjX8+86n1dzY6xNVkrkttfXWgHiqYY0jyQ4ZFmxHO6QAM8czufn\ncktWVsrIQZlA22dYnOOvEQOi2sBhU37XVVYaC9ZTARRjSBOSlUprzh0A7t+v/vzIRyrn1picw5gN\njRTg16WUhRzzox/dZA6b8z89BZ5/vvpvAYddcg5zAD9WVsvkHAY/ALlyDgkAaeUlNsFa2/kYGZ8c\nqw2wvfHxN7aOCTjPOQSAyWCy1SeplAXJQvnXqKvle3P+wdIZRh8/QNDGtMUwKWuEYZdkblZgZx3L\nMqTZ2D1vAVHNe8+4lVobH72iF2QXW3MOPVnpswfP4nB2WP9Ol7JgZLUGC8E8+wwrzLg6Mut6tV61\nmvHI3MznMSLnUJuXPzfGkGYNslasEYzH5LiZslplA4UB9Mw9oxQRzLuPyIPbYAWNvOWUnEM5F9sH\n4JlDcSu9e/9uK3OoPftbrGCvwN//6r9/3qdtY0xhDplSFleGNJlbLhaGlZU2wUFbHwB4/euB7/zO\ndLfSLrlZcqwYZohh4aRfG3PoB9FthjSxjB8rGc2RlyfnswALcM4c+8xhU1Z5fLY5dedOmDnMxQp2\nBX6x0kvgvETF7dub9zUkKwXChjQWEE8x22FlxYysdCvn0GIOO+YcbrFrBoDQAs0N4NcSkDcBnXY+\nxiTG/0jOV3NcH10HgNodMtZMISXnMFZ6S+cdEdKyNnbRL4StjYlimLxAi2EYmJ14a7faMqQJya+a\n5wF4WWlXG/7mswh44PBMVvr45HHM1/P6dzbnkJHVht4PMfNnmUMTQFqy0rWeT+aPiV37FnNoGtIo\nY4pmDkmmymJggTBgZzYQfOYsVVaaQxHBqDiA83dkFubQYk69+avvYkV63WQOy7KswKG4lbqqzqGf\ncxja8GtlBY3vftsGQkzOoWXsI26sXZnDRxIc5mJhUmSlbSDik58E/u2/rVi0GLfSWOYwhTmNNS4B\nzuWnjKzUZw4vIlcw1ZAkpZTF934v8Ff+SnufoyNgb68yZJHrwwL6GFCza1npdArcuAHcvdu+rv1+\nAg67lrJIMdvJlXPZ72+a7bQBv1w5h11kU1b+EsDJStk6h8wu62K9qEFhiXJ73IFcoK2deuOjzZSE\nYK61xTD4wV9IVsrIYRm5FzNu2WFOBYdyLC1o981EWOZMM+RRNwb8NUuaNrU9i/5aBKq8o73BHhbr\nBY7nx7gxvoH5ar7xext7RrHGLexBK3No3NdmwG4BNm3N+uwiw9Qw6yi09ilDFkca0ngyRubed32G\n6n6lLiu1jKZkbhs5hwqopcARmXOXrRwSUTrDej/kyjmUc3U1pGk+i/LMjvqjOudwVIwwW802+rAl\naizFUKsfQSPn0GKFNeA3LIaYr+ZXzGFMy8WexMoqm33acg5v366C6CZ7kmqSEmtukgK0GOaQlZXG\nGNKwckCGgezKHIbAYb9fGbP8zb8ZBofPPlv998EBD+hjzWZSWLEustLpFLh2Ddjfr9a2Jiv1wWFu\nh93crKgGRP25tcpKM+YcMjln1nGaAXJIfifBiHY+y2ylOW6gYmqGxRAAMF1Mt8dNSKJCQSvDwtDS\nU288lilDPf+AtE6AvwoiCLmXFbT5gVYoQNhiYEOBXYyslDAkAWzAxrqVWuA4dC5/7tJW5Qrj/hjz\n1RyL9QLXhtc2wGEoQNyocUka0oTYg435EzLnFIalBj4aC0Pkm/pjYnJOQ+8H2TzR1iwzbur56NnX\nWsZtgeMNWW2IOW2AY4vtzymrzOFmzZTx0ZQFuXIOa5lvgiFN81lcrKtNn1ExquscDopN6WmS0RZp\nRmWlgdRzM54RAbZXzGFEyxVos7LKGHB4clI5No5GHFPTtR5cCtCKzYNsModt0sL5vAIFXeoc5jAS\niZEVijyUzTlk3EqPj4Fnnqn+++CgG6DPxYinPh9N5nAyAW7dAj7zmbCs9PS06gMA4zG3ZrsC6Fy5\nm9TGUOgDkCHnkAU1MQFCNbftAMA399DOx+6e++fzwaHUk6Mks0Sf2Pkz52KCaKC7rLQ5phQnWobx\nsYwbmnPT+tSy0hBz1Lj3liFNjKzUAv5tgXSTxZYx7Q/3MVvOaudSBhz6zyxtyBPIOYzZ0GDAISMH\nVBlxZmOgmQNsgaPA+yEm53C1XiFUTD1aWWEF4yL1NGSlcjzrvqmy0tKWlZqAnthkYTbhYnKSazbL\nyCfcRc6h9V5rmkMtVlVtwmExrJnDZi3E0LPffB+FmMONPqGNYwXQNo+lPSPDYojZctaZOXwkDWly\nyu+YAFGYs2afJkAYDiuQJOAwp6xUftPMZi5q/gKOh1X8F5SV3rxZgWOgkliyhjRMPhkDaC1wwJj2\ntMmFm5sDXZjDVLY3FkDmANnAOTgcDIAXXwzLSpfL6v4DwNNPb6/rnIz4RcqTtwBrGzOQKecwGtRo\nDJzv/KgBP8XcA2gAn4CstHm++WqOQTHAx//Cx2u53sZuNcMMEHOjTBlIltIKooEIaZ0hK9XKGViS\nMGEpWTkgE5BYAEFKWYi5UPNclKw0wpCF7ReshdjIOVyVK+wPKkBYoqz/W1qSIQ2Rc8gqAmLAIStR\nNGVshPkNwIFja+2fLk/rTaPQ+RbrBb2umeuoySGt6+hvnqiyStJuhjYvAAAgAElEQVSQhnWPZWSV\nXTdPmsex7r+mGqj7GEYqNfBNzDm0npHmRk3NHJ7JSm9NbmFQDGo1CxAhKzXk8oDNHJosvcHAjvpp\nzOEjCw4ZoNFVftiVOZRj3bu3DQ5Zls4aU7+fjznsIisFdHB4fFz1Y2WlRXGe45UyLwZA+XPr9drv\niSYrldYmqzw+3gSHclw5R8qaZV02c/Rp3vuTkwroDwaVI2tIVrpaVbmJQMWgfvazNjhuW/spz2xs\nvi0rK7U+SiEAyeQcWlJHFkBsSfQ67sRv5V0RAfJitcCwGOLznvy88z5k0GLtsjLHYQwXmrvwDHMY\nkoz6O9aawYEEiADCwMeQnoojHxvUswGJKRsjZaWmIU1IVtpYs6yZhvUsAucBYL/Xx2K9qCWm0hbr\nRedSFkzOYeyGDnPPKABJMFUac+SPKYU5dM6h3+tjuphi2AuDw8IVWKwW6nhiGOiyLOF6BFNlSE/N\n54MwpGFZMStPlNlk2diEIpQVZj4hYUhjbUKx+YTmRp0CtPwNWOD8ufeZwya7yGwMyX3bcvxmJOW9\nopaxqnMjGNhRMcJsOVMBtNbi/8UroD0IQ5Y2cNiUH87nwHPPAb/3e+3g0AI1bL+cATI7f5GVAhVY\naAKExeKcOcwtK2UALRP4N/u1jSnEijWZwza3UpGVFkV1/tj7kTMvMUcf4Jw53NsDDg/bnWqB6pqN\nx9V/P/EExwq2rf0chjSpDOQGODR2BwEbQDIlKEIfWwZANCUxSUCz6UYYKSttO1coF2hLWtfyOWMD\nxBiW0pLoWcwIzS4aMjYGsIojnxbU+fO3JGEWQJC1nWpIExPUA90ZtlZZ6dl9k75i7CCNZQ5pt9IW\nWS1jyFOiRFmWJpPLMEyr9YrKpWWYZSCNOZR+0+U0L3NIgCMVZJecrNSSTFobCPX9IN1KrXsPcO/0\nHBsRNWBJMaQR4Euwgmydx9Cz3wb8BBwWvYKWlTJyYcqQxs85tPIpDQZ21B/haHGEQW8QXEdaeyTB\n4UUzTG2MhuVWKrlsTzxR5WY1DWlizpWD9cnJnDaZQ8mpa+YcCnMoYCq2zmEqOLDm3tbPkjoCnKx0\nuTyX3R4ecueSPrlkpblzN4FtcBiSlco1+sqvBF772u01xOQcpj6zOfIytzZGAnkFzcC2+YL3dyxT\ndn0ZANGU1mkSJAtEsZLJLVlpg4mx5IDNcWvg2AoQt9hFQp6Y4lbKsKuxsrEQOBLmUAvqfCMVdkff\nOlYKc8gyHsyatY7VPE49JlfU944Gh4TZChsgbjzXLfdVcoBljVDyQ4OFYmSVzLlkXl2ZQwA1cxiq\nqSjnCzHv8jsjKWc3fSwwUrM5yrw2mMOA7J5lBWPeDymmZnROsvd+sJhDRlaq3Q+/T1dw6L/3gPP1\nOCpGmC6mVc4haUizwZrHvPvaTGsyvYtHxQiHs0OM+qPW3632SMpKU4LfLqYcwpz5bYthOAuOr10D\nXnpp286flfGlAL9czGkTsDaZQwGHTebwxo2LcSuNBTWarNSafyjn0JeVhsCh9Ll3jzuXnC8XKxZb\nC5BZ+5I/O5noslKZ/8/+7Pa5gPZ70iWXNlUyGyurbQu4muAoJC2LyYNLCX5anR+7GrdE5twB57ke\nzT7muGOZU8aUgpAxqgFiEyAEZKVMXiYbIAJhwCrAxgJ0/kYEw0JpfbRSFk3Gr9XVj9jQaPYB2qW3\n1rGaa1/G1O/16/E3weFitehepoHMORS2gi1ez4BDS35pHScmGNdUAwxz2O/1OeZwFWYOmXcIw5xJ\nPwv41GwOyRxaG0Msc5biVto02rLe+6pk1gPHWh/zGjFA3DdR0txTNbdSt8kcyr0dFkPMVjMULoI5\nZPKN21QTLRtj1GYFIyvtn4HDohs4vGIOvRYbILLsQRtzaIFDVlbK1sPLKT+MZY9kbj44bAKE1Qq4\nfr0ChwKmWFlpLidSCxwx52NlpSFw9Na3Al/+5fzcYllBdu3nYs5kXixz6J8rpyHPrtxa22SlmnQE\naJeWNdkcps4fU8qCMfcAEARIjLOdlXO3BRBaAskNUEeMOxS0sgFiLEtp2bkDuqy0drUMBNG+jM0K\nWAFbVqoFbE1wyOzoq+C4VJjDlpw7U1ZpBfVnDrrBMZ9JJttYuLa1L0G7jL+NOTRzDtkAseW+MEG9\n348BdZZJiAn6CabCf0ZUSTXJHJ4sTlqvc32sXoHFOl/OoXatfakjI72lmEPj2WfvPZMDym6MMe8+\nChxr82eYQzLnMMWQpplzKCy+bEjEMIeUpJphDgl1jszNlJWeMYfj/rj1d6tdMYdeY8ERY4CSyhx2\ncSs920BNcqPMxRzGykpXq2rux8eVxLRpWhMCh7kko8xx5FhaPwb4aADy1389PKaU+8qsI7kfKQxc\n29ovigocfupT4VIWbdeIzaWVf3tZNkaahjRtslJGWiYBO1vHywJHbIAAhA1QGDYv1pQjtKtrsYKs\nHDTmGjFA3LKPN5nTJoAOBNGWbIwZty8r1Xbq/RIM1tzUPmf3nzUjagukWTbDCnz9Y0lQb5lEAOdB\nW4g5pNiDGOZQM+RB2ZpLW18DA6wL8F+X6yALR4F+khUymUPi/SDjni6meHLvydbfZdzL9ZIGB5Q0\n32DhWFkps6ETlJV6zBkjK7XYNYDbZGE3Ic33g5G3bDFeMTmHFoDU0g6aOYfyLhIZZtGrpOXL0jak\n2bivysaQvykaevZZWSnDHN47vddZVvrIMocXmZvFGtKEwOHt293cSlkjlVzyw5j593rnRc7bZKUC\nDv06j7Gy0lTTFmte/rFCQLxrzmFTetoc0y5cRnMwcKG1v7dXyWU1t1KNOczJnOaS1TI5wIwhjbWr\nyQQ2dJ+Obp3NYzF5iZqsdIM5NOQ1ao0uwkiGMfeIlucSOYeXQVbqG9JoAGJVrtSNAX9uGjMix2LY\nNaAdjDJsRuyaDcrqArJSCQwBHhz6DCxjRsSwmay0LtSndqs1HDQl0LSYCpNZNiSjG9dIARH9Xh8n\nyxMz51CTlbKsmNWnnhvB5lGyUjLfmAFH1j2jNlkiFBrWxlANWAz1QY6cQxOsG6C2Ledwiznskcwh\nsTHUVClYhjTmWjNY6jrn8EpWyreLZphCzFnoOMA5OLh2rfrfTXCUIodrjimn/DDGkEZKNQyH2wBp\nvT5nDgUc+sdpY1/bxpwiK5TfGVkpO3dgM58QqP67yZw1+wDdAH2OvMxcDBxwDvomE+DOnepPll1t\nguxcwDcXu8gwh6Edy7ZE+I0+PnOoyX1iZEOkWydrDR7KYWFyaix5zcbcEtxaY/PXQvOSv2MCEitA\n9neszVIWhLRImxtjSCPn0lgYf27WjvZyvWzNIwX4jQFLwsusa/9YoTE3AzbgPAAMyUoX60DOYVNW\nqjCHvsvoFptJrEd/7Co4JN1qLYlife9hS4rV+Ts7iAaq6zRd2DmHliENw5wxwXjN5hGqAdOQxlgj\n/n2lZKXE/Nnc3dB7dkNWagE/I5+wNqwicg6pUhZkzmUbEGvLORQwVbiifn6kMbJSVjWRZEgjuZvK\npk+dc3jFHPItlWHqwhxaxiUiK/TBYRdAxzKHKSxMF/ao16uKm0trAqQQcxjrVto18PfnpslK5Vis\nrFDmxuYcto1HG/dFsN25pJfAJnN4505VroKRlTbXGZCnTEcquxidc9gmK20wh219ttxKOzJ+TPDD\nBAhyLCbYMKV1RGL+1twIVpRi/BKABm2CYDGnzWsUCiSMAJGRjflMXmhezjkU7ix/KxD4AZsBmRq0\nrFd17cq2YzB5NzGMBxXUEwxkc0yarDQEfH1WKMQIi8toiIVhGHF/7BRzSDBM1n1l7z1gr2tAZw5H\nxQj35/fVnMOe40tZMEXgmfxeBoywzGFQVtrzwFGCrDR2kyX4nXEkYCGYbB/UMDmHVh/r+fDXWusm\nbUvO4WQwARDHHG6wq4QZk5xPiw2Yb7EqKy1GWJWrq5zDmLYLFqarrPTmzep/j0bV8WPyyaxcOYY9\nu0j26Eu/FPi5nwvPXwxpZrNzt1b/XM1r2BwzC2gtAM0CyNzgUGOXU+WguWWlsZLqvb1KLj2ZcLJS\nZu7smGLzbXMa8li7g0D7bmysixwV/CjyTEai19zVNHMOQ7kXTfbIMqTRZKVG0MIE2s3jWHXMTHbV\ncCttXqOLlJVKQXFx3wu1fq+P+WquM4eerDTUTzY12sqTAFyAlIvxkGNp7FoTrPpjEgahSykLBiBo\n7GKsZDY0/2ExrA2JLOMSBmhY4CAXc7g32MPd07sqc1i4qmA4Y0jDbjBRRecTZNdb7wdFVqoxuTGA\nHuC/IW19mikO1rPGMKdMziEjYWXY1dC4m7JSuW97g716rINi0FoLsW081IZfLuaQYKmFMbySlUa0\ni2ZhWFlpGziUvLym/C5VVhpbpiKFOQzNv9cD3vOe9vm3yUq7GNJ0NdHxx80ASJY1bevbNedwVxsa\nOSXFvlvpcsnLSmNzQLUxdTEtymXI0/aB32IOA46FMcCHCX4YcMQUXtaO1Qz+grusBnPK7OhTpjUE\nA9k07TEdCw1TCitI8IOtVFkpA5CGxRCz5UwFfhQ4JGSlo2KE2WqmykpjJMUpjIecTwuim2BVxtTv\n9XEwPABQSTOjS1kkBMjN+2rluKnMYeEZEikSThNoeMHoLpjD/eG+CQ6jmEMy59C61oyywLxGxhph\n7qsPxFLnb6Um+DJ4Zm4pGyP1mEq7bIj1Lm5+H9u+MT7wk7kJOOz3+uj3+rSsNJY5tGID5r2mXetr\nw0qGeCUrjWgpzFmKrLLZRwOHzz67DY5yAB9rbjkkmsz820pZjMfVvzs+7mZIk+Iy6Y+bYarYjQG5\nr35fVlaay0gndkMjV04qsJlzCLTLSstyewMlhjnMzfilSqqbstJWOQshL7EYP4Y92Qq0CXBEy5SY\nnMPAR5JhDpldf2tHmw6QCBmfH7RYdvaheUkf37hEA9DWzjjDeA56A5wuTylwGAo0gOreLtaLVtdP\naaP+CLPlLCgr3br3hqSYcitlXQ0NabY0WbcCDoUNFcMeqpQFGSBbEjVW5hwEh70K2DKyUivwt87l\nb3rkYg4tQ5ocOYesrFRbR9KHyafz349a/hor4WSY05ATK8NSN8sqWbmCycwhsRFBMafG5mFbKYui\nV2DSr4KVold0N6RhmMNQbBD5LQpd65vjSobYVVb6SILDlAAxNogE4mSlAg5f/WqeqcnFHObKOWSZ\n02bOYVEA+/vAyy+351zmCPwtAK2xgv6xaHBA3HvAzjlMddDMzYrFSor3qs24VlmpbB74x4uRS+fK\nlcy1MbIxt0BOlf+RaHvBs4yf9WFngB/NwpDmLhsBYsiQhpAWMjvaMcFfMgvFmlIYeWeMac+GKQNz\nPxTGd1AMMFvpzGHRKyjm0DKtEeYwJCvduveEpLgrI+wfizFjqsd0th6vja7Vffw1QhfCNgJkZvMk\nVVYqRbxZiaLF5oTyLWXcplsryRzuDfZw7/SezRyybqUJ71CZGysrVe89ASKY+8r2EfMjwC5PFHqH\n+PeVYjMZyWhgY8TvY61HBqxr4NBnRKWPLyut6xxmYg798YT6xeZ3agBa5hG6PlbbKTh0zv2Ec+7T\nzrm7zrn/1zn3X3q/fYVz7j85546cc/+Xc+5zGv/2R51zLznnPuuc+xuN3553zv2cc+7YOfdx59xX\naOPIlZvFSB2ln+VWKuBADGlu3erG9jG5WRft6tiFOZVrtLd3Dg5ZQ5qYvEwmnzClTxs4bIK+kFtp\nc43kNEnZVZ/QxogPDlkAzWx65Lr/ufIyt9Z+gBlpBsjNF/wWS5dQvJ0BR/5uJZvDwdQ5tIJoIJx7\nQY3bAJDMNYo15NHACCOtYxiWWjZFsBl18GcwhxoryMhKrWAc8JhDVlZqsOahe+/gUKJEWZYUgNaY\nZTlPPaazeyLMIYANeVm2nEMFHLCmFJbMV2SlFgtjyRgl73K+mgcBGwOOWeZwf7BfrSHFkMba0GDV\nB7SsVPKNjbXGMocWc5wKDhkDLWatbbyvjDxAi83yNxmsPgzbzRrSBJnDlu+wvLcKl5k5ZAxpvONY\nm17L9VKdv/zb0+Vp6+9W2zVz+N8DeF1ZljcBfC2A/8459w7n3C0A/weA7wfwOIBfAvCT8o+cc991\n1v8LAfwxAF/jnPtO77j/9OzfPA7gBQD/4uyYrS1VVtolQG72CwXRb34z8C//ZRV4MrJSZjwAH9jn\nYg7b2CO/hdgjqYfHykq7gJoUWanFHPZ61W/isBkymmFyDmNltamMcI4+miEN0C4rbVsfDOhrni9X\nrmSqac1WziHDHGrSugT2hN0ZZsCR/7Flcg5pWWmIOTQAK5VzSACNLXmmFSBq14g05WBkpRYQ9QMk\nIFyfcFAM8uQcnslKGeZwsWoP7Fvzbjqw5s652vWTdZlkQARwfk/e+dw767/z3YNDpSz8PqEg0r8G\nIXDAsqI+C0MxhwqTaQGNcX+M0+WpDg4JcBzDHAJQmcNhMVQ3PRjgw6w1GTcj45S1xjCHmmEVyxyy\nACp1w8+XVZvgWJMne6y59nxQ15qQ3avMoWsvZSHtYHjAG9Iw6QTMpqh3HO0747+vQ/OXpr2v1X/X\n6V91bGVZfrwsS4GxDkAJ4A0Avh7AR8uy/KmyLOcAPgjgbc65N5/1/RYAf7ssy0+XZflpAH8LwLcC\nwFmfdwD4YFmWs7IsfwrArwF4f2gcqZLR2AA5xBy2gcNeD/i6r9vukyorzcWedJHVxspKgbyy0hiJ\nohjSWH1C53Ku+n8Z00XLSnMxXrGyUtaQpZlzGJKVdnFqbY47V65k6rPflJW2yVmaO4itzKEBWLZ2\nvS1WMFFWyuzEM8EfIy1k8yktcNi8RhbIZgNExtE1SVZ61sdyKxXZmCYdGvRsWWmdcxgIaoHqnlDM\n4ZmstDXnsJHjx5gxWeAouU8DsMo9+cAf/wBWP1D9/aA3MIFfz/VqNpORFlpmREA4V0z6sYY0DIjQ\njjMqKkZYA4e5cw4BHRxO+hMcL4555jBBUl6zWQbwYcERkC4rZVg6URZYYwbCm3DCsMl4rM0zljUP\ngUP/OIxqgr3/rd9hT3bbPN+10TXekKYDcxjaFLby6IHquZgupur7Wtrbn3672aet7Zo5hHPuHzjn\njgH8JwCfAvB/AvgCAL8qfcqyPAHw22d/j+bvZ/8tv30+gN8ty/I48PtW26XzI8DJKi0QkcJ2yrFi\ng/+uMs4UQx5hmLrWOewKaP1+TB/rWsu4Q4xgrCFNKpsVw3ix+Y2MpLqrrLRLzmHKBkqX60iBw4Bs\njtlBtABLk13LwZyZZRqsXDAi+NsCCAHmMEYOq4FDS6YTGyBa0jIqSPCCaLWUBQFWtXsGVADBCiQK\nR+QcnjGHmjxVQERIVurLQYGwtMra9ADiAkSLEW+TlTrn6n/DsILS33RsNEw5mA0d/zgaw1KXslDG\nw7BQFHPYy5dzuD+odomf2n+q9XcAmAwmOJofmYwowMnuWUk5I4e0wBFgyEoN0M+yi7I+qHI4yiZc\nidLMN/aZbCbfNiXnkJHM+u/ZtuvUc+c1R6WPf75rw2utslIL+NHMYZshDZlzOCpGOFmcqO9iAPiF\nb/8FvPCuF9Q+obZzcFiW5XcDOADwJwH8FID52f++1+h6COAsA2/r98Ozv2v7rflvt1ouQxaWOYsx\npGn2iWEpY3KzcuQTphrStPV57LHqfw+H2+PJBXwY5jAlL9EfNysrDeUcxoD1XGx3LtkxsA0ODw7C\nrHHsuZrnSwF1F5HfCbR/KJof5LbAZQvUGG6lSbJSQsLaPJYmQaJyagxw3ASsFuMXlJUSrGAzGO9a\nUF3OJwGZHLvZmkBDY48Y2ZQWRAEV63U0P1ItzbPmHCqyUh9AAcq9j3TsS2HXttZjS3Df7/XrIFED\nY75k1GIONXaNkZXKOlKZw55dysIHEaF5WYywjJtx6mWYwxvjGwCAZ6490/o7UDGHR/Mj/n3FbAwZ\nz74F2Mx77xqqAeX+a+di7r2/eWQBSEDfGGM2j2LAsQYOGZaW2TxjlCVNsO73eetTb90CdMv1En0X\nkJV669qSnsr52nLtre8+UG3WnCxO1PsBAO98zTtrFj62td+dC25ltW34fzvn/gsAfx7AEYDrjW43\nANw/++/m7zfO/q7tt+a/3Wof+9gH8c//OfCxjwHvfve78e53vxtAPIBiA0RWVnpRskK/n+TC5cip\nYgFr2/lCOYeve131v2Nkpcx4GEMahhX0ryMDWFhZKZNzyIC6XEZLXZnlNqOlweBcVvpFXwR8+tP5\nZKWxMucU5rTLxlBQOtL4IDeDkp7r1XIWtkZXEPgYOQw+m0PvMrM5hwmJ+Uxg539INQauPo5VEoS1\nszfYVcuNj5GWWZLRWn6lBFFAFdjfn99XiyGzdQ6tnMNxf1wzhxaIkAC3eZ1iA/bUcg9tQVtzTH7u\nEWPKwTKHKaUsZEyWrHS6mOpSR4KFEkZ4tppxhjSJzOGbHn8TAOCJvSdafwfOwaHFiAK8YRVTpsJa\na9a9lzGpz76RJyvPDgOgGNk1oKtGWNk5C45z5ByazKFhSCPzWq6XGBbDjfOVP1gFyr9957e3AV3H\ndc3k2m/kHCrP/qh/xhwSstJm+/CHP4wPf/jDZr8HAg4b5389gI/iLIcQAJxz+6hyET969lcfA/A2\nAL949r/ffvZ38tvrnXP7nrT0bQD+t9BJ3/KWD+Kbvgn4+q/f/PuLKPcAhGWVzSC6q6zUAnT++QQc\ntrXYnCqWYWrOS47Txh7J+FhZaSzjlYsVTJGVhtxKc2wOdGVOc+abNmWl4zFw4wbwZ/8s8NrXboNj\nZu4pstIuDDyz1lhZaat0xAM1oY/NekmAI2PXdwtktTk/Nsw9GCfO0Ji2gA8jK7XYowBAYplTHxx3\nvY5+P8YhT3PjY2SlscBHkxdN+hPcm+llAcT5UTtO0SNyDv1SFqGSBz19l72Zb8qUcrDyoKwg0l+P\nbffOZ3uZ4JfOOQw9+4TMmWUOD9eHdKCtreuiV+BofrQT5lDypLS1tjfYw0vTl5KVDoxqgjakIeSg\nrKRcA6L+vbfuK/MMAfqzxphRiayYkTCH1r702UXOIdBYs4FvtW9IE/ymEeuaVcxYYwaqTbg70zum\nrLSt+YQYAPzQD/1Qaz+dk8zYnHNPOue+0Tm375zrOee+EsA3Afh3AH4awBc45/6Mc24E4AcB/EpZ\nlr919s//VwB/2Tn3rHPu1QD+MoD/GQDO+vwKgB90zo2cc18P4K2o3E9b20Xnb4XyrprHiZGepoBV\n/3wxgXYOaWEbMG7OTY5VFMD3fA/wr//1uVtrjKw0BdBKP5YVtACknC9GVmrlHKbMPxe7FpO3Cpyv\n634f+Mf/+Py+WrLSGLl0js2BnLLSpiFNq3RE+SABHHvASiYZVqyWIJEfW7bOYWvwA1vGR+dcWtfI\nFVjDYDs7BIhWnosWIPkW6rlkpVrQNhlMzJpxuZjDupRFQFYqx5HrHWIOmd3zGqwzOYeEPFVaUFZ6\nxuSnGm5YAHIrYFfYHHEi1ZhDxpDGkvEBVUB6ODvkDGkSmcM3PP6Gmr0JNSvnsPnuM2WlsOtlmqxx\nDlkpmXNnbQzU6oMI6aX2zrI2hiS/1Xr2a2VFizwT4ObPvPt8ABlihS0TpaasVNvMNZlDr0/oWFuy\n0sB6rHMOOzCHbNslc1iikpD+GCpQ+vsA/puyLH8GAJxz7wfwD1Axfv8PKuBY/cOy/B+cc68D8Otn\nx/lQWZYf8o79TQD+FwAvnx33/WVZ3g4O5ILzt7rk3DGGNF0DVjmWZbaSC2j5gX3bvICwrPTNb67+\nX8azS0MaBkCzzKE2/y45h8x6TAE+saxgysYI41bKsKbNfinjziW93Zq/YUhD5x11dNpjJXr+rjeb\n4G/lHFpOnHIcOX/suJmcw4u4RlYhbKkrp8lKGeMOsY9PKYIOnAf1z157NtiHzTk8XZ6qILMuZREw\npJHjaGuEkRT7/RhW0JKVWky271bKyua6AsgN1lwJ7OmcQ6OUBbuORsUI92f3OUOaROaQaZP+BMdz\nzq2UBuId62X6fWhDGkNSbslKTZbSN20x3mmAzlQxOYejYoT5aq4++/Lud3DJOYe1IsCQ+AswDH1D\nZM22gbVQLcTQuQCOXZR+raoiIw0EOJeVavcjte0MHJZl+RKAdyu//xyAz1N+/6sA/mrgtz8A8B52\nLKmBdmzeVYpbaY4cQL8fk5fIzo0Bx7GyUu1cKZJJBmiwANpiDpvMKeNWauUc7sKtNGbtx0iqU82Y\nWCCea9y53g8AWoOy5u6gFiBU5+qecxhbLJyticXkHGofSSZosz6STGDDXCM2GGcDxOl6GiUrbevX\ntI9vaz3Xq50oVeawP8Gn7n+Kyjm0coqmi6kODvt2yQNLWscG7D4ryGx6WIGmtDY2c0NWarDCkgsW\nupZyb7WcXEZWKoBVm9uwGNabFcy61u7/uD/G7ent9DqH/trvIIuTNhlUpSwY5lB7zzLKCh9Aq8Y+\nitEQ0LhGhqTcAoci4WSYM+u9D+jgsH4/KPdrWAwrcEgwh1jDZNY1JpM122GuYxZZaQfmkDGk0WSl\njFtpSrs42HmJWy6zmZi8qy4BMmu2EmPcwbh1WnOLZQ5jZKXaeHLJKi1Zbep1tGSl8u+aOaddcw5z\nsd0xaz+FOcy19i9q3KmS8qasVNsdDH3cuxiytPURd0j2Q2pJ9KicQ3JnXOvD5F0xgQ01ZjIYZwJE\n2dE3ZaVn8w/tsvuyMStAsoL6yWCCu6d39ZxDghkYFAMcL47N2nMnixM7QPSYw1ZZaYRbKVPHzWIg\nGVnpBsNg5BNapiQagGxujKiy0hUhKyVLWZjM4ZnbLQv6g+yJkZPMthhDGlpZYOSuMsY+LHPISMoZ\n1phhhJlrZElmrfdDDQ41MyovJ1tjDuU6Ws++pRqwNth88Nd2Lf13NaAAv47MoWZIo81tVIzUjZEc\n7ZEEhxcdRHeRlYZyznLJStlcudzyQ405ZEx7Yg1pGECbmmSJ2MoAACAASURBVJcZk3MYktWy9z8W\n1ORgvHI9H0BYVhrDGrOy0lQn1hzXiJKVEkEUy9LF7nrnMu5Qcw6tnfEeyRx6AIGxobdANpW7qATj\n/jXSgigJIhlZaQhEMbIxWlZajKmcQyunaNwf4/7sfhD0AcD10XXcm90zA0S5J20BMmM0BDTWdUIt\nREZW6peysFg41pCG3RjZhazUWtdAdf+BMDhssjAmc6iAKKZNBhMczg5NkCHn6qoskHEzQIsBI9Y1\nYo7D3HvmOMw7VM5Hg8NVWFYq42Zk1wzwZWtTqsyh9y3e2sjtXV7m8N7pPUwGk9bfc7RHEhzmcnWk\nA8QW4BPLLobG41z1W1lywC+HIU0MmxViDpm8s1hDmhRWyO/HXkcGZLfJRYHt+YcYxlybFTGgPwVk\n55KVxkqBy4gSLTnyMun5t30AGruDFOPVMeewPpbFsPTiZKXamJidcTOIdDYQbV4jihmwHAsJ0x51\nh915TpSEtCzEHLLBHwMOJYi2wOFsNbPB4TyccwYAB8MDnCxOMOlPTFdHAK15d1tgnZBCpwbIG0Fb\ny5iapSyYfEKNOdQ2ELY2RkKyUrKUhSUr9dluBhyG5Mk+C0MxhwqAZtqkXzHiofFsyUqNTTjmHWoC\nP0tW2mAOtc0zaxOKrXNoMnBnpk7yv0Nzs2TnAg7nq3lwA0k2KyzDpnW5pjZh2M0j6zoC7dLjtpzD\nrsyhrz6Q82mqIquUxd3Tu/UzeRHtkQSHrPxwl8xhV7dS5zYBYgrjFQt8UwxpmJzDXLLS3HmZLLsI\ntIM+oH3+qaUs2PlfJMhuboxYjDiQlnPISIFjn9nUa3R+z9oL3DO757HgiJbWJTIsltyv6cRqfkgJ\n5pQBdQxYZXI32QBRBQdrrhA0oAeILDNguUxO+hPMVrNaFtjWmJyicX9slsSQQE07jiUrZcE6w4iz\ngbbPHLaxGl1KWah1LpUNBEY14I/JYg7nq7m6ZpnjABUrLP21eQG7ZQ5DAfIGcxh4RprX2qqpaUkv\nTVkpAY5jZKWWaYslO2eeDzmf5VTsy0pDzKHkwFqyUibn0HJile+MyRxG5BxqChWTOfS/n0RsoL3X\nRkUFDif9K+Ywa8tl7pHCHHYJkK3gN5Xx6hJEpxjSxABoLWC3AFTOvEyWXewiK+1qSJSL8YqVC3dl\nDpuyUivfktnQYEC/dqxc12hjzZbtBe6bzKElqwx9JHqut1m83thBZY07GAAJoPVYTeBnykoNaRVg\nyEEN4MdIFJvHMQNEa0e71MGBH5BoslKTGSCMKwBbDijnO12emiDTYiClacfZyrtq3P8tsG7IfM08\nWWJdb+UVtclKI0pZMMwxszGkzZ8uZWGYFvm5ixpYuza8BqD9uffnBeyGOdwb7AFAcNOjqSwwDWlI\niaIlz2WZwxB7xrCUvmFVSi4pqz6IyjlU8o0HRbVZoYFDCoj7zGGCm7OVc9iaj2zkE1LS07O5dYkN\ngOqd/vLpy1fMYe6WAvxY6elFSOtS2ROG8boI5pCVlbYBaEtW2sXRNZVdta5jF3DY1ZQlll3MlZd4\n0YY0sTmHrMz3oq/RxtonHftCO5EWeyDF60u070LW4yZZQQpArleq1M3KJ/OPA+g7sVaAzBrSWNex\neRzK4EEL/tY2OKhzDhVZqQAIKmBVgnrJS9FA3bAY4mh+pDqaWnXummMLNf96h9xKLdAPbLIeKdJT\nPyAD2p/b2FIWlPRUk5USstpYt9LQmJnjANgIkrV5Sd8LZw7PWJOgzJXI3YxSDTD5hJayoMGaa5sD\nDHNoKgsy5S0XrqDqHFqGNCIrNZlDI+fQf4a0a82oBrRvMS0rjWQOg5snRGwAADfHNwHgChzmbinA\nj5GeNgPki5SV+uNOZbxyMof+NXrQstIuzKHVh2FpgXZWTPowzGksm9VVDhnLwKYwhzk3RnLm0saC\nQ+s6arvnFHPmM4dE0JLaxwSQfjDOsAcEM0IZ0gQCZAZkNvt0PY7fjwkQY2SlFnOoMhVG8AecywGt\nEhRaDTsgDhxOF9Pgb/71bpWVNpkzopRFSvDbJk9uCxK13CT/WKtyhcVKMeRx+gYCK6uNNaRJOQ4A\nHM4Og7/5xwF2lHN4tumhyUp9IGqqDzRZac9WDchas6SnrKSevmeBMJ5ROrDvELaUxWw5Uw1pBECq\nm2fEtfaZwxSjpS0TpcaxRJ2z8b3OxBwGAaTRBwAenzwOABdqSLOzOoeXqaUAvy45hxYrBnR3K/WP\nlSufDkhjDns9YFEpcJJKWRTF+XFY4NMV9Pv9cvUB4pjD5vFySWZjAXTKtWaYw1i30hyy0lx5mczz\n4ctKLclk6OPG5BwC5x9SqzA7E0TnApBM8BOVc6nkN0YxhyHpKekOSRnSGKwQsC0rDdU6Yw0nrMDu\n6YOnAYQZFvnNMpup3UoDgZ+0H373D+s5h01ZaUtA5t97poB3isOsf5wQm9Osc2gF7Zoph2Vas7Ux\npMlKmVIWK72UhchTLUMauSehRm0M7ZI5JBQBzEYV0GCzrHxjglnW3se+PNVaZ6nKAlaaTstK12c5\nh4qsdLFaYFmk5RwyJkqdZKWBZ3+1XqFX9LIxh5pixu8TWke39m4BuFjm8JEEhyzwu0g7/1iGhZWV\npjAsXSSaoQB5NgvPqzk36dcGoOU4uYxUGMYvRx8LHDZltQxzmIvxukhZKbP2c7qVMms/F8hm+9T3\nPvDhYqQjW3InwpGNkUMyAFJlBY38rWaAGMo5ZE0ZgPD8m0F0V3mqfy7T2e9s/lYBZ0ZWWpZl8BrF\nuJVaQb2AQ8nRamujYoSXT19WgR/LHP61//yvqb9ba6Qpq7Sk0O7s/1r7kGy3BRCapSys4F8t5eF0\nEMFuDDFyUGEO5bitfTzXU43J+8lv+Em8PH05+DuzMeSD7BBrzjZhTUI5h00nSuY9a4EoKw9O+jCS\n4sIVqlxelVU6uw4qA47YdwhbyoJlDsfrcVrOYc+rFWpca5M5tFI8zq71oBiomwxRzCHJrIfGvT/Y\nBwD1fqS2RxIcstK6HGAE6C4rZYCo349hDlMZFp/NYwFkrpy7VJmrOLpetFupPzdNVtpkzzTmMJXJ\nzgEyGZAtfyf3a1c5h6zM96JlpRs5h8QHgGEXS5RJ0kLpo+UvsX0sBtIyGwGq+c/XcwCGrNYACM3A\nzmQOCWMba5eZzWFhAsTFumIN2wLE2DqHWlAv4PAdz7wj2EdkpddG14J9JoMJSpSUrFRrlltrm8yz\nrcn8e65HrX2LNQfC6zG2lMV8NTeLxYfyrrY2RgLzp2SlZ0yNdo1YWelz15/Dc9efC/7OSso1SXFM\nE+YwxJ5sSAbJTbiUZ03WWmpOKisrpd77BGvOvEMKZ5eyGBUjTJdTOLcty5aWLeeQlJUy72vNkAZo\nAEhtw9fPJWUAZAKzLt8MTb6f2i4Odl7ixkrrupZFYGWlscwhw57k6KOdL1Z6miortaSOTODvHFfu\nI8bRNZU5vKicwxxrNgVkNsfdVVbq369c9wzYjWrAZw6ZDwDFeBmGG8yOtmXKwewy19LTBMfCDVkp\nwZ6E5r/RBxy7aB7H2Im35F4+K8SwiyHmJFeRawB49tqz+I53fAeev/F8sM+oGOFofmTKSgE9d5Fp\n/vUOuZVu5IExubSWw2xEHpjG5EqfUEAq7InlxqgBhObGUJJbKVHKYtAbmPJUpkkQLe7JluGGVgid\naTVzqNQ5BKBKK+XaCouf8qwJQFBBDSE7Zzb8KDdjQgbPvkPYUhbH82OVDfZzDtWSKMx71pCVssY+\nTQfRtvFsPPuM2QwjPWU2jo2c3E/e/6T6e0p7JMFhLmZgF46Nu5SVdpl/iiFN8xpZzJkFWHIwp6yx\nj5UDGgMOQ4wmC9hySyZTQGazX9c6h87xjHiOdR271hhZqSpBiQBQtMU6sROdUu7Cz2+kAm1GVkrk\nXYXGxLCLjETPP07qLrvPCoX6SBAdyjeUcVOlLMDVKPvQ134oeF+BM+aQyDkEgGEvDRxuMHUtQbL/\nfLD5tkn3rAlWQ7JSopTFuD/G0fwIg94geL3r8hKKYyElKz1jM7UAmS1lwTCHVpMgupb6BiST2r2P\naXXOoVG/Myo3z1gjFmBhwRhjyMKAQ8YghwGQ1r1ncw6PF8fqO0TWo8UcWt+0jfxOYzPPeodaOYdb\njDhjNmMwh8FzkW6l0j5x+An195R2JSv1WizDwDKHbcFmW4CsBdG5gE9O4w7KsTHAHMbmXKYE7P6x\nGBCRA0D687fcSuUaNc8Z64x70bLSmGutMYfNtR+S3rIyXxZAAvx1TNk88t1KgxIU6yPRZM4SDTck\niLZYGABUEXgm5zDEDPhSrpQAOdaQRvsgM9IyJpBiZGMSRGvFomOCPyaIsNqosN1KGddTpvm5QG1s\n9pasksi3tda16dhoyUq9UhYaiGDyMuvyEoHNE4btBjbNb0LsmZxr0BuomxUni5PkdcSufU1SHNNk\ns0KT1sXk7jLSfFNZUNrlFSzZOSsrZdyMGSksAw6HxdCsgyprX8tblvW4LMOsMcsKWtLTGHkuEP5e\nN/tkYQ5DLCUhzZb2G//1b9TvyYtojyQ4vGhpHcsc+n1CRdBj3Ep3zRyyslKLOSzL7rJS5p75c2Ny\n01KdYZs5hxZzGGJXGVYw54ZGDta8eayuslL/fKx7LMNk5jLkodY+4cQZ+kg0pWUpgEWOxRxHyxeh\n5KmEKQWTe7EFEEIlKIxcQeY6Ns9lBYiMRJEyLVGKRccWuU6R5wEec6iwgk/tPwUAplup1XxZbUjC\nyeSAyv0oUFBsjgZY/M0KRlYaOh8bIM9Xc+r9YJWyOF2e1nNoa0wpi0ExwGK2SF5HGyYhxtoH0sGh\nbBr8zsu/Y46JAREqK8hsDHkyTlVZoKx9GQ8jYc1V59DqA1RrdrqYquvj+ug6bp/cVmWlInNWZffe\ntbauI/MuZq4joCh9fFmpxhxahjRNAGkYsWnPEQC8+dabg7/laGnbjQ9py1XKIoY57OJWyuZ4xTBe\nrBwwF3MYA3w0WSWTc8gAtlSzmRjpKRAGPr5bqZaXmYPNywV8YiW8Mrcukmr/OCygZ+4HqxpIuR8+\ncxiUjBp5BbGmHGxgQ7EwCX0oU4pGMG5dI7Z4PcMudgWZ/vlUuZMBfIBz8GP1yZVzyDSGOXxy70kA\nwMHwIOlctQ19IEBkAjZg835Y5S4scLCRB2e4bFqyUkueO+wN6wDZChC1uQ16AxzPj9USJWwpixyy\n0mExrCWcu2AOAeCb3/rNeO+b3hv8XTZZWPmlmW9sSUZZ5lAJ/BmzmXpeiTl3TB/g3GxG63NjfAMv\nnbxkM4frRW3G1dYYtn+DOUxgYJvfqyBzaBjIbaWKdDSbYVRFu2pXzKHXusjvWOaQkZVassoUs5kY\nVoydG8scmvI7ok8OWSkLIhgwEgOOLLdSDUBfhGQ0B/BJAccsOMxlENRFLt0VZG/lHHbMK/ADRK2G\nof8BDLE+vqyUYWEsyWhMncOgbI5wNbQC5CbIZq6jBTIZ+aG1E824lVqyUoYZYPOFmDbqj0wnUhnr\nMwfPJJ1LwIjq6heRA8rkJTK5YkB4PbKlLEbFyJSVDopBXVDc3BhRpOD9Xt/O8fKYw9CYmZIYTPNZ\nIZY5tAw3rPZP3v9P1N835JcGiEjNS/SNfax8wpCkuDlmRlaaIjtnpemj/gjThQEORzewWC/U2nuy\n9k+Xp8F+hSswK2f6JhxpWsPKcwFdMmrVOKWZwwhDGm097qJdMYdeiw0iGZAJdA+Qu7iVpjBeOXMO\n/eNYslJGesqCQ2v+bD5hrpxDRlbK5GXmkozmkpWmMIesrJTZ9IjpI3PbWc5hQl6Bz2ZoMqVY6ZDl\neqrJ2DaYQzbnMEFe4xvytLqMkjmHG/lbAZApIGONcGDnM17WTnQOWWmOXCm2CftkSUZHxQjvev5d\nSefyTUJCmwclSlouTTHrRo6XFbT5OYcWc3g4O6QcG0PlLprrOnSuUb8CopohS80cKmtWgG8O5lCT\nywL5mUOrxT5HKYY0jPmPvPs0p9ZYcMgYsqRK00fFCCeLE5M5BCqQGGqSczhbzoLgkJLv+7mb2reR\n3MwDwiqFrZxDBvh1ZAWZtIxdtSvm0GsMe8BK1JgyBbndSnMwXjHBL3MdWeBjSU8ZcJhLfpiDXYxx\nK2UANMvm5WDFWACZy5DGkpXm2PSIzSXueo025q6YrVgfpCZzpsmUWOmQ5kTKykpXpZ27yEhnLKZm\nw5CGzBUMSUat49R29meAhAmiGbdS1ar9rI9mSHO6PKVAf44dZgEYltnM6QunSecBNpnDtmvknNsw\nN7FYQWtd1wGyweYACAJWtpTFuD825bkirVuVq9Z+zY2h0Pwn/YnNUp4xh5T5UeI6KnpVUXetHl5z\nY2xX4NACGjH5e6F3cW00pMlKe/bmkTz7DuH876xGO4Q0fVSMcLQ4UsGKrENrPZrMYUTOYaqsNLqU\nBSEHpZhDYuP4QctKH1nmMIfzIxMgApystM2xsdc7L3PAMl6pxh0CjoA8DBMDjlJlpSy7msvR9aLc\nSq25MXLQrn1i8xJZMJZDVpq66dEllzjHNdIkk/4uY5DxipDWMZIophC4JmNjchcZq3qGqWHyrjbY\nRTIv0WJOtWstzACTw8LkeGl5N1QdM2cHUWzbG+wBSHciZdqGrNRgmJjgT5WVEoyPv45CY/JLWWhj\nonIOPeawjfXb2hhSgOi92T0151DOdbo8DfYbFAMsS920hW2WccmumUMf+CUpAggJtwCfECMs45EN\nHevZ197psh5zmehY4HBYDHE0P1JZamklSvU4FjiU7wOTc5g8fxdZyiITc8hsHF/JSh9Ay1XKgmUO\nuwbIYtKSI+8qxq20DD/b0cwhE2inyEpZB82YnMvL4laa0yAph1w4RlYq/do2PWJkpTEbGimy0i4M\nrLmhEWIFI9m11J1fSg7a42WlbM6hJiu1mEMm74oxpNlgDo3cTes6Sk4R45CnSUYZWakELWweVGoQ\n8cTeEwB2Cw41x0LakMeQS/vsqsW+A4qstNiUlYbGFONWGgIRzY2RIHM4mODu6V31ngkzogXjuWSl\nQPWMTJfTnbiVMo0xd9lwNE0wpPHNVoLg0HMrZWSlQUlxMcJ8NVdBb8zGockc9m3DKmmHs8Pgb3uD\nPUyXU3M9WvP33UpTUi7YUhYXwRxqQLQsyyybNSntkQSHOfPpGOawLfj33SoBm2FLBT45WbGY/K0U\nWWVOWWluiaIFIC1ZKeNWGisHTTVSybEx0jwWszESqnMYW8qCyaVNMeSJNmMKfGwYZqCZK8fsjlqB\nDZ1PaLCCbM4hlU+p7MRaslqGXd0AmYklQXzjDsut1DKbWZV6n41SFkbwlyOoFyfSXTOHQemtt9YY\nuTT1fBi5SUB4zUbJSiOYw6Cs1FAWAJWs9N7pPZXNcc7VgDXUL5chDXBeD++yMIdMvdBY58+gIc1Z\nfifFHBqyUibnUEAmJYVNfIeI0ZLGUku7fXI7+Nv10XUczg5VcOjLri3mMDnlgixlwaSBWMwhk7vY\nc+f51g4u+C3eRXskwWFqKYsYqSMQlpWytd4YUBeTT5fq/HgRhjQpstKYPLhcEsUcslK5/wxzetFu\npbHsGrtGcuQc5sylTVUE5JCVMsEfyx4wH3e2lMWq1GWlNXOYWueQYE4ZcMywq80+1tzMnENLVkow\nh8JAzpazYLAVU8rCchpk2pP7FTh87c3XJh2HaZZbqfSxZGPMhkbNnhDyMyAshd5wKyUMaVLAIWtI\nMxlMcG92zwT0k8EEAJLWGttqWSnrVnrBOVUUC0VIT5n3AyUr9RivFNbcOWcWpmfAEcOuAV4dVGOt\nfcmzX4IvfNUXBn8XcDhbhd99w2KI2XKm3g//OqaoavrOzjncyEsMbfgyzGETiLasR6kzvFgvHqik\nFLgypNloXRgGq4+cLyXvipXW5WIFWVaIAdAMK2a5Vcq5uubl+f2YPr1eGgObS1bKgLGcstIY5izF\nkCaGNWc3NBjWXMadS1JulbKgDWmMUg5MvhQDINV8QtKttJbxMTmHgQCIkfEVvQLzxRyAwQoaALIp\nK7XmZroRGrJSCfwX6zA4HPfHmK1mVYAUYHNo9sDYPWfbzfFNABdfWBmw3UqBhtTPYPyYXFqLWZc1\nQtU5VFifvcEeXp6+XDOxbU1ARM/1TEMa7f6P++OKOTTYHDmHJmO08tfYNiyGlayUYA41WXGuVvQK\nLFaLyrRIKQnCOj5r10gYr/lqrkvKM7iVyvm02oMMoJW/1xhIgKuDCgC/8O2/ELzOwLnx1b3Teypz\nOF/PqZzDVEDPALZmKQsqL9HqY7CiUurmQbYr5tBrFxFoA2HmsBkgp0jrWFDHMCypzGETQDKGNAyA\nSpGV5mIF2WvNzI1hTnPlysUep2088u/EJMkC2TK3to2Ri8g5TJFCx15HZl1TCe6Bj0RTMplisV6z\nghbwWxvAT/ISY3IOLeZU+dhu5CUS0lMGQGqg1pLnMjXjBBxqkrlxf4zpYmoyDDlqnbGt53r46W/8\nabzlibckHYdp2WSlPiNubHqYDIPBZLOlLG6Ob+IPD/8Q10fXW38HCOawsfY1WWnI8bR5PK1JPqXm\nnsu2WOZwF7JSAeLae82SnjLyfdk8WqzsnMNUWSlQAa2TxUmSW6f0s2pODosh7s/vUxsR1hq6PrqO\n29PbQXDo51Oq3xnLtIY0pGFyDqPVMFYfrdRRr1Adf3fVHklwmCvnMMWQpsmehBgmNlcwB3MYG2in\nMIcsgLSYQ9aQJgZo5ACZlqy0yQpazGGu0io5zGZYqSeQJiv1r7V1X1nZNZCnziGzHjVrbOZjUwMf\n5KnRxQI/1dwjJueQmJvWx9/RZQB029z8QJsFxylgbFSMMFvOKllpIEga98c4XZ6aslLGTCEXOASA\n973lfTuRMjFupSybw7LdajBGMNk+c6g9a8LAPjZ+rPV3IN6QJnSuWi5qOEhaDIQvh9TqMzJtUAwu\nXc6hJdHbyO9NNKSZr+aYr/O4lTLM4dH8KHguVnYu4NACotq5YppsnKjMoSHP9dUH6j0j36EAJwfN\nyRxqMufF6kpW+kBaLlMOxpQixLLE5hwysrnUnLuLYA4tVkj6MNLTHMxhDgAZY9rDyEpZ5rDr/LuY\nzeRgV2VuXWWlMes6p6MpI2Gm1jVhex0KSFhpGQNqGFkplU9IGoD4O6jBUhaWW6srzPk3g2imD1Om\nw5SVKsHWqD8yZaVFr0C/16924g1ZqWWkkctlcpeNcSut2RwliGZkzhubJ0bgD4QBi1/KQrsnAg7l\nz7Ym8kMHZxrSaPd20p/Ux9OaxUAwwTjbalnpJWIOZ8uZ+nwwzsC+jDH4fjhTFjCKgByy0lGhAzaG\n7QRA5bjJJhZTysJqAg5Dx5L1qMnuc8pKrW8xu+GZnTl8wLLSRxYcphhOxDA1Eow3j9Ul5zAlV/Ai\nJHosgGZklSl5eQyoiWG8cshzfRAxaIkRGeY0Nucwp6OpBfxi5t9VVhoL/HKwiwC/eRQC9JYhDVPL\nqAkgU6QzMQwLG2gzOYehAIj52DaZmhADazE+MTmHjOFELSsNfLiHxRCzlc4cAuf5YtbOuHauXMXL\nd91iZaWWG6FVm9N0ouw1JMxtstKCl5X6f7Y1CX7lv9XxKEBUmJcQA+MfT2uyoZELHB7Pj3VZpVHq\nJmcTWal2no1nzXAiZfrMV/Pgsz8shvUGU6qsVKSeGnPIyEoFHGt9ctZBFXAYevbFaIcy9rHyEo0+\nsaUsaABp9LHea1ey0gfUUmWlVsAeC3yYfjkYv1xBdMw1YlhBVlbKSC9zAGhWnptSyiKWFU3drOiy\nodHWYtg8IF+dwxSwyoLM2JxDCxyGAomtj0QAHLHMIZVTZeUTEoE2I9OJlcyqAJopU8G4nhrSU3Zu\nAhAoWanCHALnTpMhWanvoMiM50EHEjEtxq00ddPDdz1VjW2MNStjtuqPiZzUAoez5UwvZWEYVgHA\nrb1bAICn9p4KngvgmENZsznAocpmufM6bhrIztUk0DZlpcZaGxUjzFYz05DGAtlMTnJMzqElK43J\nOdT6yHreRakb2WDo9/omk2vJSpmcw5hSFlSqCMMcGptVl8Gt9JEEhyl5V7E5XiFQc1GOjamMl3Wu\n2GvEGHewAHJXuXJajhvLLvpupSGjoRhwzAKWXFLoHJsMQPu6FiZdA5D+mFLBaiwjzoBsRgrMlKnQ\nrLF9d0SG8dPyfFh2kT0OIxtLKVPBgGPW9ZTJOWSMS0TGSbmVkszhZco53FWLdStN2fRgWKGmAYwm\nK5WgPnQ+hjmUnNNUQ5qD4QGA9JxDAT45mMNBb2BKHf11fdHr1jekCTXGkGbUH52XVzAMqzRDGjFb\n0UBNFHOoOIjW+baGsiAGHDJ1Dq02W87U36VshnYu2TxUn+tegTX0b9pGmQqylEXXOoeMYkb6XclK\nH1BjJXo5cry0INJnT0IBci72pNcDFos8+WQMwxKTT5fLrZQBbKz0tG08zT4pzCGzRmJz3HYhK41x\nfdXGLZsjMv4cOYepz0cOufTGPSNlpW19/A+SlS8VBfyMsgBMMG45xEm9RIY51T62MYY0GhBlcg79\n+QdlY4Rb6ahfBdqL9aKWYrW1cX+Me7N7uDW51fo7W8riYQaHjKyUcSPU5MKM9LaZcxh6HpfrJRbr\nhZonV/QKzF/QAzsBh4Ni0BoAN9lui/mT5yTUNHMcIL+slGEOd5FvCHiyUuV+MIY0Ul5Be9ZimEMt\n35aVi5s5h87ezAOq9WYZoORkDi3QI6BX2/SomUNr45RwPM5SyqIDc6htwl7JSh9Qy1nrrO04zp0b\n0WiBr5wHyCMr3UUQHesgyoAaVlaaAg5j2KMQWPP7pJayYIBfrKNrLulpDnkys65lc4RhzXMAP9aQ\nJpeslDak6QiOpB8DDq0PqQTalimDyS66zRyvVgdRQlbaZA5DH2Qm51COY5XgyGFIU7gCZVnWwX+o\nTfpnstLEOocW43EZW6xbKWM4odWwk2tkbbAA4XUkCx7wpAAAIABJREFUpSwWK10uDFTrRLsfk8EE\n02W4lMmGU7EB/L/97d+Ob37rN6vj+Y4v+g71d0vmGtNMcNg7r/O3K3A4W3GGNIxcXHs/buQcBtaI\nLytlcg6Z8hKhay3vXuv9wMgYc4LDf/b+f4Zf+s5fCv7OlM2QdZQqK93IOdSYQytVglDMsMwhA9Z3\n0a6YQ691yTtq6+PL5jQWSgJJjT2JdStNzadbLvMwh7uSlbKghgXZF8EcdpWVxs4/JZ+Qua9+P+Y6\nAjpzaIFD9lxsPmHudc3kHJr5CWQSfErOoS+ZZD6kVjBu5UvkyPFiZLVMbpYv0VuDM9sJBn89O+fQ\nOYdRf4Tj+bEKIixZ6YasMrCHK1JHDaxfxhbjVspselg5h7GyUos5TK0FKMxhz/Vaj8XIzqX9o/f9\nI/N83/r2b8W3vO1bgr/nlJVKLq3FZi3Wi50wI4ystM5LVd5roghgDKso5pCUlYbeVzKm0+Wpes8Y\nyaj00d4hOcHha268Bq+58Zrg78Ic3hjdCPbxDatSNk4LV+B0fQpA2ahlSlmQufZMLnHhKrD+oN/p\nD892Y8bGuBGyMr4UFsrPO8zhVpqaTxfLHKYY0uSUlTKgJsaJlGEOrbxEP+ewq6zUHzNwsfmEXWSl\nTP6eJSvV+sQYyYSO0RxzihQ2lu3VpI4AzgFboiGNJYekZaWEAYjF5jCysa1gPDR/w5CGyc3yA+1U\nsx0WjI2KEY4WR3bOoRJEy/xVpoIAq5exxbqVMsGflXNoykoJQxphfFJrAU76E5wuTzFdTOtyFH5z\ncChRnkuzMwSJqjwxo6x0b7CHu6d3VTbLwe0sp0pKWWhA1N+ICMrFvfqlGisKQAVsMbJSi12Vc1jg\n0GIFGaZq3B9jf7CPJ/efDPbJ1UbFCKtypcpKNxQBCd+0Zj5h6DvDfIuimENjg/UyyEofSeaQldaF\nAkRWfmexUDHSuhxSP4YVi8k5ZMAIlZtFyipD90wkvLmcSFnm0GKEtbkxslof+DASzhTgF2PsYzHi\n7L1lmUO2ficjKWZzDlPWoyUrBezgt5kLkSKHrBk/Qw5p7rKK9NTI8yhRqnbuG7mCSvI+k3NIyUrJ\nwsOUrHStG9IAm257oXYwPMAf3PsDSlaaalxx2VqMW6kF6iTnMEVW6ss4l+tl67od9UdYrCqzkVQ5\n5Lg/xnQxhYNrLUPhnKOY7FxNSgfkkHru9fdwb3YPT+w9EezT7/VxsjjJYm5iNZmbyRwa+a3CHFoA\nen+wjzvTO5RbqSUr1YAowJWXKJxdUJ1hF51zOPq+o+DvOZvMR1sfUitUZeCIbyPDCvoqHoYVDG0g\n0XUOCYfdXbRHEhwy+YQM45WDhbLkh0yAHMP4WUE06+gJ5GMOU2SVIuEtS27cDNBgmMPU+8rIav17\nxgCxXLm0zBphrpHMrausNDbn0Nr0YPrIeJjryMhKtQ9XLb8kDGmsADkHu0gb0iggSwJbbfecSd5n\nmFMfHGhyHz9/K2X+g55d6wyoAsnjhS4rvTG+gRePX6TqHDKSyQcdSMS0WLdSTQptrUdGVupvIIQA\nUl2/MoOsdDKomEPnHCaDbeawHhPhNJmjjYpzeaImY2TaZDBRmUOg2mQ5nh9nKahutVExwnQ5NQ1p\nrLU2KkY4WZyYz/5jk8fwqfufCtaejJGVarmLAHB9eL0+ZqgxklGmzuEuWw0OlfXhX0fmu6dtim5I\nPTsayfisYOgdwrCLcr7F6kpW+kBaSgmKWObQkrsx8kPWrZSV3zGSQQaMMMwhK9FjAGSO+ecAGjHM\nISMrtcAxK+FkgF9Knqh/PuYaaYZMjKw0JufQYlet6xib38lIoRnWgzGkYSRxFONHjodhF1WZlisq\nQ5ZAYLNVgiIA6izm0DlXXyeNOWTMPRhWlKlzCJybcmgg4sboBo4Xx8GSB4xJButqeNkaY0gTKyu1\nWBgmJxc4C+xcOzisS5QkykrH/TGmyylOl6etslJ/TNazlqPJPchxHktWClTX8nB2GARQOduwGGK6\nmOqGLIwhTX9UO2hqAPrm+CaW62XwuZZNhtlyppaxYfJbb4xv1McMNVZWepnA4bXRNQD6vMREyZKV\nUoY0hqy074hSFg3msBUcRjKHV7LSB9BYOWRXAxD/WFoQHZNzyEr9UoFfTD5Zijy3i/QyJfhn5bms\njDOGOUwFxwwwBtJLWVjj8Y/FzF+udWhuwhwydQ5T5NIxjDDAPfsUgCRkpYxDGhX8GlJPAT6WBIep\nqci432lunVtSz445h8A5YGNKWTDXyJJxSr6QlZ9zf3ZfBRESPGqlLKgcyDMmM2RacxmbBONWIXAm\nB9Zas6xbqRXYSX26kOw0pokhTUhWCmwyp7sK2kuUycdgwOGoGOFwdrgTWemoXzGHqYY0lgurNCkb\nIsCt7VzL9RKny1MbHBqy0usjmzkUEGXmHF6CouvSZD7z1TzYR/JkLWOXGFlp0NSMMaSJZA4tVdFl\nqHN4OVbDjhvDCrLSshRGg805ZHMFWcYrJfBnmMNcslLGkMY/VipzyABoH2iwEsVUt9KYnMMUsM4w\nh7FgLGVjJOZczLpmnyGRKofGA3CyUks6oslK2ZxD35TECn5VaR28PtqYjXpgAMEcNndQiZxDDRwv\n1guqlAV7jYLsQWEHJEDFCt6e3jaZQwDB3KwYWelilS513GUTUMu4lbKseYqstJlzGJSVLmdmnUOm\n9Xt9ODgcL46DstIHUaakLPOAQ8tBc9Q/A4c7kpWeLE6yGNJoZSOkWSYxzrnzUg1GvrElK5V3iAUO\np8upunkkMsbLAg6lHc3DOY60rJQxpIkoZcFITynm0FAyXAawfrlWw46aBvzYgF36MLlZKcYlMexJ\naq4cE0T7Y2ZcX5nrmFNWugvmMLesNDXnkJGVWteazTlkWbjl0l77jCENC/xS2U5m7Q8G52OmwCFp\nSJPCnG2wR4ZsznJIoyQ4RB6UxRwKoAXScg7lWIvVQt3RpWSlhIxRGB+Lzbk5vqnmEwLnzMKtvTBz\naIEDYU0X63Sp4y6bjFsz5bCeD4BzdGXdSi1wKExFDlmpjMv/s9mYTZ/cTa5BShOZLCMr3ZUhDcUc\nEoY092c2OGRyNusi74H5D3qDaq0ZslKWOZwu9JzLOi/xAcsYmy1kMgWcl19Rv7GMIQ2xCbmR4kE4\nmlI5h8a4L4Os9JEEhyx7EgqixRkzNTcrRn6YQzLKMpBMEK3NPxb45ZCVssAv9Tp2MaRhrlHXe89I\nRpl1Lf9O8gRTQJSwghZzGOPUmwLWY5h1be79PrBYcGMGoBpuWEErW+dQHDRZ+V1qweDaIU75cPV7\nfUyX02AQveX+Rkh5mDxAK+fQLGBtXEdxLLTAsQC/g+FBsM/n3vpcAGFZKcNmiJlCjhIEu2ziNGg9\nH0wurXWNaFmpF9i19ctpSCPjsn63Nn1yt8V6kXwMxkFTZKW7yDkc9UemHNTPOdQMaVblynzOXnP9\nNeaYLOZwMphgupyaz/Vjk0rCuj/YD/aRWohWzuVlcMdsNqsWoqg4UgxpmgXuW4FfjyhTQTCHG9+9\nwKYocH4/HrSs9JHMOWTYk1Af3x2TYZisYNNnmDT5YY48sBzyO98gJyS/6wL8UmSlPmBl3FpTAEIs\nc5gqK2XBkZzLkkvn2EBgWGqLOYyVlaaua5ZZ144jzKFVd7J+ppXdWB+MhHLlSpR2eQWPOetqfiN9\nGLdORuo2KkaqIQvzkZRdVpG6aXXsRFYaMq0BYM6fqRkozGFZlio4vjmq8gk1cPie170Hv/0Xf7s2\nX2ibl3U/xJB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HOWWl7HWMYQ5TmNNYWWlX9lXOBdjj3mXOYcza18BhrvIbMYY0p6fpstLa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\nLQSfv/1tt++dBa0R177oWuYWAomerPqy4BZ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s8Iu6508Fbsr//cT8Mf/cMWY2UNdaeipZ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\nQwkQuwgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d01ed0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data preparation for the training set will involve the following steps:\n",
    "\n",
    "1. Filter the original dataset to include only that time period reserved for the training set\n",
    "2. Scale the time series such that the values fall within the interval (0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create training set containing only the model features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (744, 1)\n",
      "Test data shape:  (1464, 1)\n"
     ]
    }
   ],
   "source": [
    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
    "\n",
    "print('Training data shape: ', train.shape)\n",
    "print('Test data shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01 00:00:00</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 01:00:00</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 02:00:00</th>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 03:00:00</th>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 04:00:00</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 05:00:00</th>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 06:00:00</th>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 07:00:00</th>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 08:00:00</th>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 09:00:00</th>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-10-01 00:00:00  0.15\n",
       "2014-10-01 01:00:00  0.11\n",
       "2014-10-01 02:00:00  0.08\n",
       "2014-10-01 03:00:00  0.08\n",
       "2014-10-01 04:00:00  0.13\n",
       "2014-10-01 05:00:00  0.28\n",
       "2014-10-01 06:00:00  0.56\n",
       "2014-10-01 07:00:00  0.71\n",
       "2014-10-01 08:00:00  0.75\n",
       "2014-10-01 09:00:00  0.78"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train['load'] = scaler.fit_transform(train)\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original vs scaled data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2FbAL+DRwTrp9ZUS0A2cC15GMGJsHLCjuFBFLgRXAcyQd949USipDgZdxscHO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\nVlNOLGZmVlNOLGZmVlNOLGZmVlP/H7l30l6heHXqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d00f2f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UXih/T/b78WzmRxXHyDRK7VIbyvvi34B35h1/HffDUkr/DO2reHyxvB92TPR3\n00NtmJlZoqK0QZiZWcE4QZiZWSInCDMzS+QEYWZmiZwgzMwskROEmZklcoIwM7NEThBmZpbICcLM\nzBL9f8cVx5taXt2wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d00f2198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[energy.index < train_start_dt][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also scale the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-11-01 00:00:00  0.16\n",
       "2014-11-01 01:00:00  0.11\n",
       "2014-11-01 02:00:00  0.08\n",
       "2014-11-01 03:00:00  0.08\n",
       "2014-11-01 04:00:00  0.10"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['load'] = scaler.transform(test)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement ARIMA method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An ARIMA, which stands for **A**uto**R**egressive **I**ntegrated **M**oving **A**verage, model can be created using the statsmodels library. In the next section, we perform the following steps:\n",
    "1. Define the model by calling SARIMAX() and passing in the model parameters: p, d, and q parameters, and P, D, and Q parameters.\n",
    "2. The model is prepared on the training data by calling the fit() function.\n",
    "3. Predictions can be made by calling the forecast() function and specifying the number of steps (horizon) which to forecast\n",
    "\n",
    "In an ARIMA model there are 3 parameters that are used to help model the major aspects of a times series: seasonality, trend, and noise. These parameters are:\n",
    "- **p** is the parameter associated with the auto-regressive aspect of the model, which incorporates past values. \n",
    "- **d** is the parameter associated with the integrated part of the model, which effects the amount of differencing to apply to a time series. \n",
    "- **q** is the parameter associated with the moving average part of the model.\n",
    "\n",
    "If our model has a seasonal component, we use a seasonal ARIMA model (SARIMA). In that case we have another set of parameters: P, D, and Q which describe the same associations as p,d, and q, but correspond with the seasonal components of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Specify the number of steps to forecast ahead\n",
    "HORIZON = 3 if demo else 24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s look at an autocorrelation plot of the time series. The example below plots the autocorrelation for 48 lags in the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uLGdQxTi3ihY/6t27N9tttxNDhvyTDh0+Lck1586dy8SJE0tyraiNGzeIbt2+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\nEBGRrP4fFWzrdr8en60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d047b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autocorrelation_plot(train[1:48])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a significant positive correlation with the first 4 or 5 lags. That may be a good starting point for the AR parameter (p) of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot of energy load over time (see above) shows that the time series is not stationary due to its seasonality (daily peaks and also peaks in August and February due to the increased energy usage). This suggests the a certain degree of differencing the data might be necessary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L0lVXWQkeUO8IDJFGBIbIhcAQCMGFE2mVK2O4cqW0fbv0yiuVPSagHIKNZyQCQzQ+ms8g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\nNSo5g5fmwItEW1asWuXvmVRaFFmypL5tKygl7QyHHVY9Y5j0S7OV3vt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zxk8iKo1AR36l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\nOQJDAAAAAMg5AkMAAAAAyLn/B0P17moQLFFTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d0451198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['load'].diff(periods=24).plot(y='load', figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even after differencing the data by 24 hours (the seasonal frequency), we still see a seasonal trend in the data. \n",
    "\n",
    "Selecting the best parameters for an Arima model can be challenging - somewhat subjective and time intesive, so we'll leave it as an exercise to the user. We used an **auto_arima()** function to search a provided space of parameters for the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Model search takes a while, so don't run it during the demo\n",
    "auto_tune = False\n",
    "\n",
    "if (auto_tune):\n",
    "    auto_model = auto_arima(train, start_p=1, start_q=0,\n",
    "                               max_p=5, max_q=0, m=24,\n",
    "                               start_P=0, max_P=2,Q=0, \n",
    "                               seasonal=True,\n",
    "                               d=1, D=1, trace=True,\n",
    "                               error_action='ignore',  \n",
    "                               suppress_warnings=True, \n",
    "                               stepwise=True)\n",
    "    print(auto_model.aic())\n",
    "    print(auto_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 Statespace Model Results                                 \n",
      "==========================================================================================\n",
      "Dep. Variable:                               load   No. Observations:                  744\n",
      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                1630.982\n",
      "Date:                            Fri, 28 Sep 2018   AIC                          -3249.965\n",
      "Time:                                    20:31:42   BIC                          -3222.498\n",
      "Sample:                                10-01-2014   HQIC                         -3239.360\n",
      "                                     - 10-31-2014                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.7789      0.021     37.993      0.000       0.739       0.819\n",
      "ar.L2         -0.5345      0.041    -12.988      0.000      -0.615      -0.454\n",
      "ar.L3          0.1335      0.053      2.502      0.012       0.029       0.238\n",
      "ar.L4         -0.0871      0.047     -1.855      0.064      -0.179       0.005\n",
      "ar.S.L24      -0.1439      0.036     -3.968      0.000      -0.215      -0.073\n",
      "sigma2         0.0006   1.78e-05     35.138      0.000       0.001       0.001\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       43.83   Jarque-Bera (JB):              1316.38\n",
      "Prob(Q):                              0.31   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.91   Skew:                             0.41\n",
      "Prob(H) (two-sided):                  0.47   Kurtosis:                         9.58\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "order = (4, 1, 0)\n",
    "seasonal_order = (1, 1, 0, 24)\n",
    "\n",
    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
    "results = model.fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we display the distribution of residuals. A zero mean in the residuals may indicate that there is no bias in the prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SmVVv1iwbo71liw2ZW7kSeOaZhicwXXSRdaiPHWsXbIie56dJE6uH3nab3Q6f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\nwKqMO9KzQqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d03e0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BhprZn8zsOTM7s7ONVnyWkZk9CrT+BGWEN75/LrF6qTfEPsB+wPnu/ryZXQtc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d02fb9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0\n",
      "count 100.00\n",
      "mean    0.00\n",
      "std     0.06\n",
      "min    -0.20\n",
      "25%    -0.01\n",
      "50%    -0.00\n",
      "75%     0.02\n",
      "max     0.28\n"
     ]
    }
   ],
   "source": [
    "# plot residual errors\n",
    "residuals = pd.DataFrame(results.resid[0:100])\n",
    "residuals.plot()\n",
    "plt.show()\n",
    "residuals.plot(kind='kde')\n",
    "plt.show()\n",
    "print(residuals.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will perform the so-called **walk forward validation**. In practice, time series models are re-trained each time a new data becomes available. This allows the model to make the best forecast at each time step. \n",
    "\n",
    "Starting at the beginning of the time series, we train the model on the train data set. Then we make a prediction on the next time step. The prediction is then evaluated against the known value. The training set is then expanded to include the known value and the process is repeated. (Note that we keep the training set window fixed, for more efficient training, so every time we add a new observation to the training set, we remove the observation from the beginning of the set.)\n",
    "\n",
    "This process provides a more robust estimation of how the model will perform in practice. However, it comes at the computation cost of creating so many models. This is acceptable if the data is small or if the model is simple, but could be an issue at scale. \n",
    "\n",
    "Walk-forward validation is the gold standard of time series model evaluation and is recommended for your own projects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a test data point for each HORIZON step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>load+1</th>\n",
       "      <th>load+2</th>\n",
       "      <th>load+3</th>\n",
       "      <th>load+4</th>\n",
       "      <th>load+5</th>\n",
       "      <th>load+6</th>\n",
       "      <th>load+7</th>\n",
       "      <th>load+8</th>\n",
       "      <th>load+9</th>\n",
       "      <th>...</th>\n",
       "      <th>load+14</th>\n",
       "      <th>load+15</th>\n",
       "      <th>load+16</th>\n",
       "      <th>load+17</th>\n",
       "      <th>load+18</th>\n",
       "      <th>load+19</th>\n",
       "      <th>load+20</th>\n",
       "      <th>load+21</th>\n",
       "      <th>load+22</th>\n",
       "      <th>load+23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load  load+1  load+2  load+3  load+4  load+5  load+6  \\\n",
       "2014-11-01 00:00:00  0.16    0.11    0.08    0.08    0.10    0.16    0.28   \n",
       "2014-11-01 01:00:00  0.11    0.08    0.08    0.10    0.16    0.28    0.43   \n",
       "2014-11-01 02:00:00  0.08    0.08    0.10    0.16    0.28    0.43    0.57   \n",
       "2014-11-01 03:00:00  0.08    0.10    0.16    0.28    0.43    0.57    0.67   \n",
       "2014-11-01 04:00:00  0.10    0.16    0.28    0.43    0.57    0.67    0.72   \n",
       "\n",
       "                     load+7  load+8  load+9   ...     load+14  load+15  \\\n",
       "2014-11-01 00:00:00    0.43    0.57    0.67   ...        0.69     0.70   \n",
       "2014-11-01 01:00:00    0.57    0.67    0.72   ...        0.70     0.75   \n",
       "2014-11-01 02:00:00    0.67    0.72    0.73   ...        0.75     0.82   \n",
       "2014-11-01 03:00:00    0.72    0.73    0.72   ...        0.82     0.84   \n",
       "2014-11-01 04:00:00    0.73    0.72    0.70   ...        0.84     0.76   \n",
       "\n",
       "                     load+16  load+17  load+18  load+19  load+20  load+21  \\\n",
       "2014-11-01 00:00:00     0.75     0.82     0.84     0.76     0.68     0.56   \n",
       "2014-11-01 01:00:00     0.82     0.84     0.76     0.68     0.56     0.41   \n",
       "2014-11-01 02:00:00     0.84     0.76     0.68     0.56     0.41     0.27   \n",
       "2014-11-01 03:00:00     0.76     0.68     0.56     0.41     0.27     0.19   \n",
       "2014-11-01 04:00:00     0.68     0.56     0.41     0.27     0.19     0.12   \n",
       "\n",
       "                     load+22  load+23  \n",
       "2014-11-01 00:00:00     0.41     0.27  \n",
       "2014-11-01 01:00:00     0.27     0.19  \n",
       "2014-11-01 02:00:00     0.19     0.12  \n",
       "2014-11-01 03:00:00     0.12     0.10  \n",
       "2014-11-01 04:00:00     0.10     0.10  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_shifted = test.copy()\n",
    "\n",
    "for t in range(1, HORIZON):\n",
    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
    "    \n",
    "test_shifted = test_shifted.dropna(how='any')\n",
    "test_shifted.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 . Predicting time step:  2014-11-01 00:00:00\n",
      "2 . Predicting time step:  2014-11-01 01:00:00\n",
      "3 . Predicting time step:  2014-11-01 02:00:00\n",
      "4 . Predicting time step:  2014-11-01 03:00:00\n",
      "5 . Predicting time step:  2014-11-01 04:00:00\n",
      "6 . Predicting time step:  2014-11-01 05:00:00\n",
      "7 . Predicting time step:  2014-11-01 06:00:00\n",
      "8 . Predicting time step:  2014-11-01 07:00:00\n",
      "9 . Predicting time step:  2014-11-01 08:00:00\n",
      "10 . Predicting time step:  2014-11-01 09:00:00\n",
      "11 . Predicting time step:  2014-11-01 10:00:00\n",
      "12 . Predicting time step:  2014-11-01 11:00:00\n",
      "13 . Predicting time step:  2014-11-01 12:00:00\n",
      "14 . Predicting time step:  2014-11-01 13:00:00\n",
      "15 . Predicting time step:  2014-11-01 14:00:00\n",
      "16 . Predicting time step:  2014-11-01 15:00:00\n",
      "17 . Predicting time step:  2014-11-01 16:00:00\n",
      "18 . Predicting time step:  2014-11-01 17:00:00\n",
      "19 . Predicting time step:  2014-11-01 18:00:00\n",
      "20 . Predicting time step:  2014-11-01 19:00:00\n",
      "21 . Predicting time step:  2014-11-01 20:00:00\n",
      "22 . Predicting time step:  2014-11-01 21:00:00\n",
      "23 . Predicting time step:  2014-11-01 22:00:00\n",
      "24 . Predicting time step:  2014-11-01 23:00:00\n",
      "25 . Predicting time step:  2014-11-02 00:00:00\n",
      "26 . Predicting time step:  2014-11-02 01:00:00\n",
      "27 . Predicting time step:  2014-11-02 02:00:00\n",
      "28 . Predicting time step:  2014-11-02 03:00:00\n",
      "29 . Predicting time step:  2014-11-02 04:00:00\n",
      "30 . Predicting time step:  2014-11-02 05:00:00\n",
      "31 . Predicting time step:  2014-11-02 06:00:00\n",
      "32 . Predicting time step:  2014-11-02 07:00:00\n",
      "33 . Predicting time step:  2014-11-02 08:00:00\n",
      "34 . Predicting time step:  2014-11-02 09:00:00\n",
      "35 . Predicting time step:  2014-11-02 10:00:00\n",
      "36 . Predicting time step:  2014-11-02 11:00:00\n",
      "37 . Predicting time step:  2014-11-02 12:00:00\n",
      "38 . Predicting time step:  2014-11-02 13:00:00\n",
      "39 . Predicting time step:  2014-11-02 14:00:00\n",
      "40 . Predicting time step:  2014-11-02 15:00:00\n",
      "41 . Predicting time step:  2014-11-02 16:00:00\n",
      "42 . Predicting time step:  2014-11-02 17:00:00\n",
      "43 . Predicting time step:  2014-11-02 18:00:00\n",
      "44 . Predicting time step:  2014-11-02 19:00:00\n",
      "45 . Predicting time step:  2014-11-02 20:00:00\n",
      "46 . Predicting time step:  2014-11-02 21:00:00\n",
      "47 . Predicting time step:  2014-11-02 22:00:00\n",
      "48 . Predicting time step:  2014-11-02 23:00:00\n",
      "49 . Predicting time step:  2014-11-03 00:00:00\n",
      "50 . Predicting time step:  2014-11-03 01:00:00\n",
      "51 . Predicting time step:  2014-11-03 02:00:00\n",
      "52 . Predicting time step:  2014-11-03 03:00:00\n",
      "53 . Predicting time step:  2014-11-03 04:00:00\n",
      "54 . Predicting time step:  2014-11-03 05:00:00\n",
      "55 . Predicting time step:  2014-11-03 06:00:00\n",
      "56 . Predicting time step:  2014-11-03 07:00:00\n",
      "57 . Predicting time step:  2014-11-03 08:00:00\n",
      "58 . Predicting time step:  2014-11-03 09:00:00\n",
      "59 . Predicting time step:  2014-11-03 10:00:00\n",
      "60 . Predicting time step:  2014-11-03 11:00:00\n",
      "61 . Predicting time step:  2014-11-03 12:00:00\n",
      "62 . Predicting time step:  2014-11-03 13:00:00\n",
      "63 . Predicting time step:  2014-11-03 14:00:00\n",
      "64 . Predicting time step:  2014-11-03 15:00:00\n",
      "65 . Predicting time step:  2014-11-03 16:00:00\n",
      "66 . Predicting time step:  2014-11-03 17:00:00\n",
      "67 . Predicting time step:  2014-11-03 18:00:00\n",
      "68 . Predicting time step:  2014-11-03 19:00:00\n",
      "69 . Predicting time step:  2014-11-03 20:00:00\n",
      "70 . Predicting time step:  2014-11-03 21:00:00\n",
      "71 . Predicting time step:  2014-11-03 22:00:00\n",
      "72 . Predicting time step:  2014-11-03 23:00:00\n",
      "73 . Predicting time step:  2014-11-04 00:00:00\n",
      "74 . Predicting time step:  2014-11-04 01:00:00\n",
      "75 . Predicting time step:  2014-11-04 02:00:00\n",
      "76 . Predicting time step:  2014-11-04 03:00:00\n",
      "77 . Predicting time step:  2014-11-04 04:00:00\n",
      "78 . Predicting time step:  2014-11-04 05:00:00\n",
      "79 . Predicting time step:  2014-11-04 06:00:00\n",
      "80 . Predicting time step:  2014-11-04 07:00:00\n",
      "81 . Predicting time step:  2014-11-04 08:00:00\n",
      "82 . Predicting time step:  2014-11-04 09:00:00\n",
      "83 . Predicting time step:  2014-11-04 10:00:00\n",
      "84 . Predicting time step:  2014-11-04 11:00:00\n",
      "85 . Predicting time step:  2014-11-04 12:00:00\n",
      "86 . Predicting time step:  2014-11-04 13:00:00\n",
      "87 . Predicting time step:  2014-11-04 14:00:00\n",
      "88 . Predicting time step:  2014-11-04 15:00:00\n",
      "89 . Predicting time step:  2014-11-04 16:00:00\n",
      "90 . Predicting time step:  2014-11-04 17:00:00\n",
      "91 . Predicting time step:  2014-11-04 18:00:00\n",
      "92 . Predicting time step:  2014-11-04 19:00:00\n",
      "93 . Predicting time step:  2014-11-04 20:00:00\n",
      "94 . Predicting time step:  2014-11-04 21:00:00\n",
      "95 . Predicting time step:  2014-11-04 22:00:00\n",
      "96 . Predicting time step:  2014-11-04 23:00:00\n",
      "97 . Predicting time step:  2014-11-05 00:00:00\n",
      "98 . Predicting time step:  2014-11-05 01:00:00\n",
      "99 . Predicting time step:  2014-11-05 02:00:00\n",
      "100 . Predicting time step:  2014-11-05 03:00:00\n",
      "101 . Predicting time step:  2014-11-05 04:00:00\n",
      "102 . Predicting time step:  2014-11-05 05:00:00\n",
      "103 . Predicting time step:  2014-11-05 06:00:00\n",
      "104 . Predicting time step:  2014-11-05 07:00:00\n",
      "105 . Predicting time step:  2014-11-05 08:00:00\n",
      "106 . Predicting time step:  2014-11-05 09:00:00\n",
      "107 . Predicting time step:  2014-11-05 10:00:00\n",
      "108 . Predicting time step:  2014-11-05 11:00:00\n",
      "109 . Predicting time step:  2014-11-05 12:00:00\n",
      "110 . Predicting time step:  2014-11-05 13:00:00\n",
      "111 . Predicting time step:  2014-11-05 14:00:00\n",
      "112 . Predicting time step:  2014-11-05 15:00:00\n",
      "113 . Predicting time step:  2014-11-05 16:00:00\n",
      "114 . Predicting time step:  2014-11-05 17:00:00\n",
      "115 . Predicting time step:  2014-11-05 18:00:00\n",
      "116 . Predicting time step:  2014-11-05 19:00:00\n",
      "117 . Predicting time step:  2014-11-05 20:00:00\n",
      "118 . Predicting time step:  2014-11-05 21:00:00\n",
      "119 . Predicting time step:  2014-11-05 22:00:00\n",
      "120 . Predicting time step:  2014-11-05 23:00:00\n",
      "121 . Predicting time step:  2014-11-06 00:00:00\n",
      "122 . Predicting time step:  2014-11-06 01:00:00\n",
      "123 . Predicting time step:  2014-11-06 02:00:00\n",
      "124 . Predicting time step:  2014-11-06 03:00:00\n",
      "125 . Predicting time step:  2014-11-06 04:00:00\n",
      "126 . Predicting time step:  2014-11-06 05:00:00\n",
      "127 . Predicting time step:  2014-11-06 06:00:00\n",
      "128 . Predicting time step:  2014-11-06 07:00:00\n",
      "129 . Predicting time step:  2014-11-06 08:00:00\n",
      "130 . Predicting time step:  2014-11-06 09:00:00\n",
      "131 . Predicting time step:  2014-11-06 10:00:00\n",
      "132 . Predicting time step:  2014-11-06 11:00:00\n",
      "133 . Predicting time step:  2014-11-06 12:00:00\n",
      "134 . Predicting time step:  2014-11-06 13:00:00\n",
      "135 . Predicting time step:  2014-11-06 14:00:00\n",
      "136 . Predicting time step:  2014-11-06 15:00:00\n",
      "137 . Predicting time step:  2014-11-06 16:00:00\n",
      "138 . Predicting time step:  2014-11-06 17:00:00\n",
      "139 . Predicting time step:  2014-11-06 18:00:00\n",
      "140 . Predicting time step:  2014-11-06 19:00:00\n",
      "141 . Predicting time step:  2014-11-06 20:00:00\n",
      "142 . Predicting time step:  2014-11-06 21:00:00\n",
      "143 . Predicting time step:  2014-11-06 22:00:00\n",
      "144 . Predicting time step:  2014-11-06 23:00:00\n",
      "145 . Predicting time step:  2014-11-07 00:00:00\n",
      "146 . Predicting time step:  2014-11-07 01:00:00\n",
      "147 . Predicting time step:  2014-11-07 02:00:00\n",
      "148 . Predicting time step:  2014-11-07 03:00:00\n",
      "149 . Predicting time step:  2014-11-07 04:00:00\n",
      "150 . Predicting time step:  2014-11-07 05:00:00\n",
      "151 . Predicting time step:  2014-11-07 06:00:00\n",
      "152 . Predicting time step:  2014-11-07 07:00:00\n",
      "153 . Predicting time step:  2014-11-07 08:00:00\n",
      "154 . Predicting time step:  2014-11-07 09:00:00\n",
      "155 . Predicting time step:  2014-11-07 10:00:00\n",
      "156 . Predicting time step:  2014-11-07 11:00:00\n",
      "157 . Predicting time step:  2014-11-07 12:00:00\n",
      "158 . Predicting time step:  2014-11-07 13:00:00\n",
      "159 . Predicting time step:  2014-11-07 14:00:00\n",
      "160 . Predicting time step:  2014-11-07 15:00:00\n",
      "161 . Predicting time step:  2014-11-07 16:00:00\n",
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      "166 . Predicting time step:  2014-11-07 21:00:00\n",
      "167 . Predicting time step:  2014-11-07 22:00:00\n",
      "168 . Predicting time step:  2014-11-07 23:00:00\n",
      "169 . Predicting time step:  2014-11-08 00:00:00\n",
      "170 . Predicting time step:  2014-11-08 01:00:00\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "171 . Predicting time step:  2014-11-08 02:00:00\n",
      "172 . Predicting time step:  2014-11-08 03:00:00\n",
      "173 . Predicting time step:  2014-11-08 04:00:00\n",
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      "241 . Predicting time step:  2014-11-11 00:00:00\n",
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      "337 . Predicting time step:  2014-11-15 00:00:00\n",
      "338 . Predicting time step:  2014-11-15 01:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "339 . Predicting time step:  2014-11-15 02:00:00\n",
      "340 . Predicting time step:  2014-11-15 03:00:00\n",
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      "481 . Predicting time step:  2014-11-21 00:00:00\n",
      "482 . Predicting time step:  2014-11-21 01:00:00\n",
      "483 . Predicting time step:  2014-11-21 02:00:00\n",
      "484 . Predicting time step:  2014-11-21 03:00:00\n",
      "485 . Predicting time step:  2014-11-21 04:00:00\n",
      "486 . Predicting time step:  2014-11-21 05:00:00\n",
      "487 . Predicting time step:  2014-11-21 06:00:00\n",
      "488 . Predicting time step:  2014-11-21 07:00:00\n",
      "489 . Predicting time step:  2014-11-21 08:00:00\n",
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      "492 . Predicting time step:  2014-11-21 11:00:00\n",
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      "494 . Predicting time step:  2014-11-21 13:00:00\n",
      "495 . Predicting time step:  2014-11-21 14:00:00\n",
      "496 . Predicting time step:  2014-11-21 15:00:00\n",
      "497 . Predicting time step:  2014-11-21 16:00:00\n",
      "498 . Predicting time step:  2014-11-21 17:00:00\n",
      "499 . Predicting time step:  2014-11-21 18:00:00\n",
      "500 . Predicting time step:  2014-11-21 19:00:00\n",
      "501 . Predicting time step:  2014-11-21 20:00:00\n",
      "502 . Predicting time step:  2014-11-21 21:00:00\n",
      "503 . Predicting time step:  2014-11-21 22:00:00\n",
      "504 . Predicting time step:  2014-11-21 23:00:00\n",
      "505 . Predicting time step:  2014-11-22 00:00:00\n",
      "506 . Predicting time step:  2014-11-22 01:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "507 . Predicting time step:  2014-11-22 02:00:00\n",
      "508 . Predicting time step:  2014-11-22 03:00:00\n",
      "509 . Predicting time step:  2014-11-22 04:00:00\n",
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      "518 . Predicting time step:  2014-11-22 13:00:00\n",
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      "522 . Predicting time step:  2014-11-22 17:00:00\n",
      "523 . Predicting time step:  2014-11-22 18:00:00\n",
      "524 . Predicting time step:  2014-11-22 19:00:00\n",
      "525 . Predicting time step:  2014-11-22 20:00:00\n",
      "526 . Predicting time step:  2014-11-22 21:00:00\n",
      "527 . Predicting time step:  2014-11-22 22:00:00\n",
      "528 . Predicting time step:  2014-11-22 23:00:00\n",
      "529 . Predicting time step:  2014-11-23 00:00:00\n",
      "530 . Predicting time step:  2014-11-23 01:00:00\n",
      "531 . Predicting time step:  2014-11-23 02:00:00\n",
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      "673 . Predicting time step:  2014-11-29 00:00:00\n",
      "674 . Predicting time step:  2014-11-29 01:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "675 . Predicting time step:  2014-11-29 02:00:00\n",
      "676 . Predicting time step:  2014-11-29 03:00:00\n",
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      "714 . Predicting time step:  2014-11-30 17:00:00\n",
      "715 . Predicting time step:  2014-11-30 18:00:00\n",
      "716 . Predicting time step:  2014-11-30 19:00:00\n",
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      "735 . Predicting time step:  2014-12-01 14:00:00\n",
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      "801 . Predicting time step:  2014-12-04 08:00:00\n",
      "802 . Predicting time step:  2014-12-04 09:00:00\n",
      "803 . Predicting time step:  2014-12-04 10:00:00\n",
      "804 . Predicting time step:  2014-12-04 11:00:00\n",
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      "808 . Predicting time step:  2014-12-04 15:00:00\n",
      "809 . Predicting time step:  2014-12-04 16:00:00\n",
      "810 . Predicting time step:  2014-12-04 17:00:00\n",
      "811 . Predicting time step:  2014-12-04 18:00:00\n",
      "812 . Predicting time step:  2014-12-04 19:00:00\n",
      "813 . Predicting time step:  2014-12-04 20:00:00\n",
      "814 . Predicting time step:  2014-12-04 21:00:00\n",
      "815 . Predicting time step:  2014-12-04 22:00:00\n",
      "816 . Predicting time step:  2014-12-04 23:00:00\n",
      "817 . Predicting time step:  2014-12-05 00:00:00\n",
      "818 . Predicting time step:  2014-12-05 01:00:00\n",
      "819 . Predicting time step:  2014-12-05 02:00:00\n",
      "820 . Predicting time step:  2014-12-05 03:00:00\n",
      "821 . Predicting time step:  2014-12-05 04:00:00\n",
      "822 . Predicting time step:  2014-12-05 05:00:00\n",
      "823 . Predicting time step:  2014-12-05 06:00:00\n",
      "824 . Predicting time step:  2014-12-05 07:00:00\n",
      "825 . Predicting time step:  2014-12-05 08:00:00\n",
      "826 . Predicting time step:  2014-12-05 09:00:00\n",
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      "828 . Predicting time step:  2014-12-05 11:00:00\n",
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      "830 . Predicting time step:  2014-12-05 13:00:00\n",
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      "832 . Predicting time step:  2014-12-05 15:00:00\n",
      "833 . Predicting time step:  2014-12-05 16:00:00\n",
      "834 . Predicting time step:  2014-12-05 17:00:00\n",
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      "838 . Predicting time step:  2014-12-05 21:00:00\n",
      "839 . Predicting time step:  2014-12-05 22:00:00\n",
      "840 . Predicting time step:  2014-12-05 23:00:00\n",
      "841 . Predicting time step:  2014-12-06 00:00:00\n",
      "842 . Predicting time step:  2014-12-06 01:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "843 . Predicting time step:  2014-12-06 02:00:00\n",
      "844 . Predicting time step:  2014-12-06 03:00:00\n",
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      "899 . Predicting time step:  2014-12-08 10:00:00\n",
      "900 . Predicting time step:  2014-12-08 11:00:00\n",
      "901 . Predicting time step:  2014-12-08 12:00:00\n",
      "902 . Predicting time step:  2014-12-08 13:00:00\n",
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      "904 . Predicting time step:  2014-12-08 15:00:00\n",
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      "908 . Predicting time step:  2014-12-08 19:00:00\n",
      "909 . Predicting time step:  2014-12-08 20:00:00\n",
      "910 . Predicting time step:  2014-12-08 21:00:00\n",
      "911 . Predicting time step:  2014-12-08 22:00:00\n",
      "912 . Predicting time step:  2014-12-08 23:00:00\n",
      "913 . Predicting time step:  2014-12-09 00:00:00\n",
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      "915 . Predicting time step:  2014-12-09 02:00:00\n",
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      "1008 . Predicting time step:  2014-12-12 23:00:00\n",
      "1009 . Predicting time step:  2014-12-13 00:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1010 . Predicting time step:  2014-12-13 01:00:00\n",
      "1011 . Predicting time step:  2014-12-13 02:00:00\n",
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      "1116 . Predicting time step:  2014-12-17 11:00:00\n",
      "1117 . Predicting time step:  2014-12-17 12:00:00\n",
      "1118 . Predicting time step:  2014-12-17 13:00:00\n",
      "1119 . Predicting time step:  2014-12-17 14:00:00\n",
      "1120 . Predicting time step:  2014-12-17 15:00:00\n",
      "1121 . Predicting time step:  2014-12-17 16:00:00\n",
      "1122 . Predicting time step:  2014-12-17 17:00:00\n",
      "1123 . Predicting time step:  2014-12-17 18:00:00\n",
      "1124 . Predicting time step:  2014-12-17 19:00:00\n",
      "1125 . Predicting time step:  2014-12-17 20:00:00\n",
      "1126 . Predicting time step:  2014-12-17 21:00:00\n",
      "1127 . Predicting time step:  2014-12-17 22:00:00\n",
      "1128 . Predicting time step:  2014-12-17 23:00:00\n",
      "1129 . Predicting time step:  2014-12-18 00:00:00\n",
      "1130 . Predicting time step:  2014-12-18 01:00:00\n",
      "1131 . Predicting time step:  2014-12-18 02:00:00\n",
      "1132 . Predicting time step:  2014-12-18 03:00:00\n",
      "1133 . Predicting time step:  2014-12-18 04:00:00\n",
      "1134 . Predicting time step:  2014-12-18 05:00:00\n",
      "1135 . Predicting time step:  2014-12-18 06:00:00\n",
      "1136 . Predicting time step:  2014-12-18 07:00:00\n",
      "1137 . Predicting time step:  2014-12-18 08:00:00\n",
      "1138 . Predicting time step:  2014-12-18 09:00:00\n",
      "1139 . Predicting time step:  2014-12-18 10:00:00\n",
      "1140 . Predicting time step:  2014-12-18 11:00:00\n",
      "1141 . Predicting time step:  2014-12-18 12:00:00\n",
      "1142 . Predicting time step:  2014-12-18 13:00:00\n",
      "1143 . Predicting time step:  2014-12-18 14:00:00\n",
      "1144 . Predicting time step:  2014-12-18 15:00:00\n",
      "1145 . Predicting time step:  2014-12-18 16:00:00\n",
      "1146 . Predicting time step:  2014-12-18 17:00:00\n",
      "1147 . Predicting time step:  2014-12-18 18:00:00\n",
      "1148 . Predicting time step:  2014-12-18 19:00:00\n",
      "1149 . Predicting time step:  2014-12-18 20:00:00\n",
      "1150 . Predicting time step:  2014-12-18 21:00:00\n",
      "1151 . Predicting time step:  2014-12-18 22:00:00\n",
      "1152 . Predicting time step:  2014-12-18 23:00:00\n",
      "1153 . Predicting time step:  2014-12-19 00:00:00\n",
      "1154 . Predicting time step:  2014-12-19 01:00:00\n",
      "1155 . Predicting time step:  2014-12-19 02:00:00\n",
      "1156 . Predicting time step:  2014-12-19 03:00:00\n",
      "1157 . Predicting time step:  2014-12-19 04:00:00\n",
      "1158 . Predicting time step:  2014-12-19 05:00:00\n",
      "1159 . Predicting time step:  2014-12-19 06:00:00\n",
      "1160 . Predicting time step:  2014-12-19 07:00:00\n",
      "1161 . Predicting time step:  2014-12-19 08:00:00\n",
      "1162 . Predicting time step:  2014-12-19 09:00:00\n",
      "1163 . Predicting time step:  2014-12-19 10:00:00\n",
      "1164 . Predicting time step:  2014-12-19 11:00:00\n",
      "1165 . Predicting time step:  2014-12-19 12:00:00\n",
      "1166 . Predicting time step:  2014-12-19 13:00:00\n",
      "1167 . Predicting time step:  2014-12-19 14:00:00\n",
      "1168 . Predicting time step:  2014-12-19 15:00:00\n",
      "1169 . Predicting time step:  2014-12-19 16:00:00\n",
      "1170 . Predicting time step:  2014-12-19 17:00:00\n",
      "1171 . Predicting time step:  2014-12-19 18:00:00\n",
      "1172 . Predicting time step:  2014-12-19 19:00:00\n",
      "1173 . Predicting time step:  2014-12-19 20:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1174 . Predicting time step:  2014-12-19 21:00:00\n",
      "1175 . Predicting time step:  2014-12-19 22:00:00\n",
      "1176 . Predicting time step:  2014-12-19 23:00:00\n",
      "1177 . Predicting time step:  2014-12-20 00:00:00\n",
      "1178 . Predicting time step:  2014-12-20 01:00:00\n",
      "1179 . Predicting time step:  2014-12-20 02:00:00\n",
      "1180 . Predicting time step:  2014-12-20 03:00:00\n",
      "1181 . Predicting time step:  2014-12-20 04:00:00\n",
      "1182 . Predicting time step:  2014-12-20 05:00:00\n",
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      "1186 . Predicting time step:  2014-12-20 09:00:00\n",
      "1187 . Predicting time step:  2014-12-20 10:00:00\n",
      "1188 . Predicting time step:  2014-12-20 11:00:00\n",
      "1189 . Predicting time step:  2014-12-20 12:00:00\n",
      "1190 . Predicting time step:  2014-12-20 13:00:00\n",
      "1191 . Predicting time step:  2014-12-20 14:00:00\n",
      "1192 . Predicting time step:  2014-12-20 15:00:00\n",
      "1193 . Predicting time step:  2014-12-20 16:00:00\n",
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      "1196 . Predicting time step:  2014-12-20 19:00:00\n",
      "1197 . Predicting time step:  2014-12-20 20:00:00\n",
      "1198 . Predicting time step:  2014-12-20 21:00:00\n",
      "1199 . Predicting time step:  2014-12-20 22:00:00\n",
      "1200 . Predicting time step:  2014-12-20 23:00:00\n",
      "1201 . Predicting time step:  2014-12-21 00:00:00\n",
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      "1211 . Predicting time step:  2014-12-21 10:00:00\n",
      "1212 . Predicting time step:  2014-12-21 11:00:00\n",
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      "1214 . Predicting time step:  2014-12-21 13:00:00\n",
      "1215 . Predicting time step:  2014-12-21 14:00:00\n",
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      "1219 . Predicting time step:  2014-12-21 18:00:00\n",
      "1220 . Predicting time step:  2014-12-21 19:00:00\n",
      "1221 . Predicting time step:  2014-12-21 20:00:00\n",
      "1222 . Predicting time step:  2014-12-21 21:00:00\n",
      "1223 . Predicting time step:  2014-12-21 22:00:00\n",
      "1224 . Predicting time step:  2014-12-21 23:00:00\n",
      "1225 . Predicting time step:  2014-12-22 00:00:00\n",
      "1226 . Predicting time step:  2014-12-22 01:00:00\n",
      "1227 . Predicting time step:  2014-12-22 02:00:00\n",
      "1228 . Predicting time step:  2014-12-22 03:00:00\n",
      "1229 . Predicting time step:  2014-12-22 04:00:00\n",
      "1230 . Predicting time step:  2014-12-22 05:00:00\n",
      "1231 . Predicting time step:  2014-12-22 06:00:00\n",
      "1232 . Predicting time step:  2014-12-22 07:00:00\n",
      "1233 . Predicting time step:  2014-12-22 08:00:00\n",
      "1234 . Predicting time step:  2014-12-22 09:00:00\n",
      "1235 . Predicting time step:  2014-12-22 10:00:00\n",
      "1236 . Predicting time step:  2014-12-22 11:00:00\n",
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      "1238 . Predicting time step:  2014-12-22 13:00:00\n",
      "1239 . Predicting time step:  2014-12-22 14:00:00\n",
      "1240 . Predicting time step:  2014-12-22 15:00:00\n",
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      "1243 . Predicting time step:  2014-12-22 18:00:00\n",
      "1244 . Predicting time step:  2014-12-22 19:00:00\n",
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      "1263 . Predicting time step:  2014-12-23 14:00:00\n",
      "1264 . Predicting time step:  2014-12-23 15:00:00\n",
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      "1273 . Predicting time step:  2014-12-24 00:00:00\n",
      "1274 . Predicting time step:  2014-12-24 01:00:00\n",
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      "1314 . Predicting time step:  2014-12-25 17:00:00\n",
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      "1321 . Predicting time step:  2014-12-26 00:00:00\n",
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      "1336 . Predicting time step:  2014-12-26 15:00:00\n",
      "1337 . Predicting time step:  2014-12-26 16:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1338 . Predicting time step:  2014-12-26 17:00:00\n",
      "1339 . Predicting time step:  2014-12-26 18:00:00\n",
      "1340 . Predicting time step:  2014-12-26 19:00:00\n",
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      "1345 . Predicting time step:  2014-12-27 00:00:00\n",
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      "1426 . Predicting time step:  2014-12-30 09:00:00\n",
      "1427 . Predicting time step:  2014-12-30 10:00:00\n",
      "1428 . Predicting time step:  2014-12-30 11:00:00\n",
      "1429 . Predicting time step:  2014-12-30 12:00:00\n",
      "1430 . Predicting time step:  2014-12-30 13:00:00\n",
      "1431 . Predicting time step:  2014-12-30 14:00:00\n",
      "1432 . Predicting time step:  2014-12-30 15:00:00\n",
      "1433 . Predicting time step:  2014-12-30 16:00:00\n",
      "1434 . Predicting time step:  2014-12-30 17:00:00\n",
      "1435 . Predicting time step:  2014-12-30 18:00:00\n",
      "1436 . Predicting time step:  2014-12-30 19:00:00\n",
      "1437 . Predicting time step:  2014-12-30 20:00:00\n",
      "1438 . Predicting time step:  2014-12-30 21:00:00\n",
      "1439 . Predicting time step:  2014-12-30 22:00:00\n",
      "1440 . Predicting time step:  2014-12-30 23:00:00\n",
      "1441 . Predicting time step:  2014-12-31 00:00:00\n",
      "CPU times: user 18h 34min 36s, sys: 8h 9min 30s, total: 1d 2h 44min 7s\n",
      "Wall time: 4h 51min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
    "\n",
    "train_ts = train['load']\n",
    "test_ts = test_shifted\n",
    "\n",
    "history = [x for x in train_ts]\n",
    "history = history[(-training_window):]\n",
    "\n",
    "predictions = list()\n",
    "\n",
    "for t in range(test_ts.shape[0]):\n",
    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
    "    model_fit = model.fit()\n",
    "    yhat = model_fit.forecast(steps = HORIZON)\n",
    "    predictions.append(yhat)\n",
    "    obs = list(test_ts.iloc[t])\n",
    "    # move the training window\n",
    "    history.append(obs[0])\n",
    "    history.pop(0)\n",
    "    print(t+1, '. Predicting time step: ', test_ts.index[t])\n",
    "    # print(t+1, ': predicted =', yhat, 'expected =', obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare predictions to actual load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-01 00:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,508.87</td>\n",
       "      <td>2,514.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-01 01:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,395.71</td>\n",
       "      <td>2,434.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-01 02:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,407.74</td>\n",
       "      <td>2,390.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-01 03:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,381.71</td>\n",
       "      <td>2,382.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-01 04:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,463.96</td>\n",
       "      <td>2,419.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-11-01 00:00:00  t+1    2,508.87 2,514.00\n",
       "1 2014-11-01 01:00:00  t+1    2,395.71 2,434.00\n",
       "2 2014-11-01 02:00:00  t+1    2,407.74 2,390.00\n",
       "3 2014-11-01 03:00:00  t+1    2,381.71 2,382.00\n",
       "4 2014-11-01 04:00:00  t+1    2,463.96 2,419.00"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean absolute percentage error over all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h\n",
      "t+1    0.01\n",
      "t+10   0.05\n",
      "t+11   0.05\n",
      "t+12   0.05\n",
      "t+13   0.05\n",
      "t+14   0.05\n",
      "t+15   0.06\n",
      "t+16   0.06\n",
      "t+17   0.06\n",
      "t+18   0.06\n",
      "t+19   0.06\n",
      "t+2    0.02\n",
      "t+20   0.06\n",
      "t+21   0.07\n",
      "t+22   0.07\n",
      "t+23   0.07\n",
      "t+24   0.07\n",
      "t+3    0.03\n",
      "t+4    0.03\n",
      "t+5    0.04\n",
      "t+6    0.04\n",
      "t+7    0.04\n",
      "t+8    0.05\n",
      "t+9    0.05\n",
      "Name: APE, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "if(HORIZON > 1):\n",
    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
    "    print(eval_df.groupby('h')['APE'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One step forecast MAPE:  0.9198698774852749 %\n"
     ]
    }
   ],
   "source": [
    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One step forecast MAPE:  4.973729251357043 %\n"
     ]
    }
   ],
   "source": [
    "print('One step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the predictions vs the actuals for the first week of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rsHXrVs/t8+fPx49+9CMMDw8jHo/jmmuuQV9fH2x+wxEREQViGikREQPDlrJmzRoMDAyg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\nMLP6KlBrMhsxHA4kUaSCc818GhkiBxLV8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DjQR7s0u4wYXrYnZPdyaxucrCIIv5Xzyn5p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ofgGgxRYjdvx2KkbwAKOzdbRxd4kqGS5YRkZxQ+SPUUlnCOiJJcyp4h/vJ6kKmzj5IWV6AS3l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QHkcgiTIZ/2W8HO8vsBOu8vOmTgbGC4xDkuXZp+z/M++N8PsYMpoUgKVn/evyhtS0spMkuV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\n3dvc6bMLYFyaFsONSSmch91zzz245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BKsjllGViYFhuIUGcPCaI2/Z0XGUNwf7EDlt2QU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\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2bChom23btrFixQpaWlqYPn06S5cuZevWrf3rn3rqKS6++GKam5uJqmpdRKawYECB4MKv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\nYCgiIiIiIlLmFBiKiIiIiIiUuf8HPMYnaDC00CsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1d01d9a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if(HORIZON == 1):\n",
    "    ## Plotting single step forecast\n",
    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
    "\n",
    "else:\n",
    "    ## Plotting multi step forecast\n",
    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
    "    for t in range(1, HORIZON+1):\n",
    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
    "\n",
    "    fig = plt.figure(figsize=(15, 8))\n",
    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
    "    ax = fig.add_subplot(111)\n",
    "    for t in range(1, HORIZON+1):\n",
    "        x = plot_df['timestamp'][(t-1):]\n",
    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
    "    \n",
    "    ax.legend(loc='best')\n",
    "    \n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
